Hi, I'm

Diwansu Pilania

AI & Machine Learning Enthusiast

Building intelligent solutions for a smarter world

Diwansu Pilania

About Me

My Journey

I am an AI & Machine Learning enthusiast with a passion for building intelligent solutions that solve real-world problems. My journey in the world of artificial intelligence began during learning about basic machine learning.

With a strong foundation in mathematics and computer science, I specialize in developing machine learning models, particularly in the areas of computer vision and natural language processing. I am constantly exploring new technologies and methodologies to enhance my skills and create more efficient AI solutions.

Education

BE in Information Technology, 2024-Present

Certifications

Deep Learning Specialization, Coursera

Technical Skills

Tech Stack

PythonTensorFlowPyTorchScikit-LearnOpenCVPandasNumPyKerasDeep LearningNLPData Analysis

Areas of Expertise

Machine Learning60%
Deep Learning45%
Natural Language Processing35%

My Projects

Explore my portfolio of AI and machine learning projects, showcasing my skills and expertise in building intelligent solutions.

Licence Plate Detection

Licence Plate Detection

Developed a real-time License Plate Recognition system using a YOLO-based model integrated with IP webcams for live video stream processing and vehicle identification.

PythonYOLOOpenCV
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Licence Plate Detection

Key Features:

  • Developed a real-time license plate recognition system using a deep learning model
  • Integrated IP webcam streams for live vehicle detection and plate reading
  • Implemented end-to-end pipeline from video capture to text extraction
  • Designed the system for scalability across multiple camera sources
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Handwritten Digit Recognition

Handwritten Digit Recognition

A deep learning model using Convolutional Neural Networks to classify handwritten digits with high accuracy.

PythonTensorFlowCNNMNIST
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Handwritten Digit Recognition

Key Features:

  • Implemented a CNN architecture with multiple convolutional layers
  • Achieved 99.2% accuracy on the MNIST test dataset
  • Optimized model size for mobile deployment
  • Added data augmentation to improve model robustness
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No₂ Prediction Machine Learning Model

No₂ Prediction Machine Learning Model

A machine learning model for predicting NO₂ levels using air quality data. The model leverages regression techniques and feature engineering to provide accurate predictions.

PythonScikit-learnPandasRegressionFlask
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No₂ Prediction Machine Learning Model

Key Features:

  • Predicts NO₂ concentration based on real-time environmental data
  • Uses feature selection and preprocessing for improved accuracy
  • Implements regression algorithms for precise estimations
  • Deployed as a web app for user-friendly interaction
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Hybrid Prediction Model

Hybrid Prediction Model

A machine learning model that combines multiple algorithms to enhance predictive accuracy for financial forecasting.

PythonScikit-LearnPandasXGBoost
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Hybrid Prediction Model

Key Features:

  • Ensemble of Random Forest, XGBoost, and LSTM models
  • Feature engineering pipeline for time-series data
  • Cross-validation framework for robust evaluation
  • Interactive visualization dashboard for results
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Power BI Dashboard

Power BI Dashboard

An Interactive Power BI dashboard for visualizing and analyzing Hospital Waiting list.

Power BIExcelPython
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Power BI Dashboard

Key Features:

  • Real-Time Insights: Visualizes patient waiting lists by department, urgency, status, and demographic breakdowns.
  • Average Wait Time Analysis: Tracks and compares average wait times across different specialties to identify bottlenecks.
  • Status Distribution Breakdown: Interactive visuals showing counts of patients by status (Waiting, Scheduled, Completed).
  • Data Exploration Table: Searchable and sortable patient-level table for operational and administrative use
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Get In Touch

Have a question or want to work together? Feel free to reach out to me using the contact form below.

Contact Information

Location

Pune Maharashtra, India

Connect With Me

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