Motivated Data Science Graduate with hands-on experience building ML models, analyzing complex datasets. Proficient in Python, SQL, AI/ML tools, with proven success improving model precision, developing interactive dashboards. Eager to apply these skills as an AI/ML Engineer driving data-driven solutions.
Nullclass EdTech
Built real-time emotion detection system by integrating voice, age, and video analysis. Used preprocessing and feature engineering to improve accuracy. Collaborated on designing engaging educational modules, performed testing, and provided technical support to enhance user experience.
Clustor Computing
Implemented time series forecasting to predict booking trends with 84% precision using statistical software. Developed interactive Power BI dashboards to support data-driven decisions while achieving 76% model accuracy through data-oriented programming.
Personifwy
Performed clustering and discriminant analysis, reducing error rate by 5%. Analyzed large datasets to identify marketing trends. Built predictive models for customer behavior using Python and R. Developed Dialogflow chatbot prototype achieving 82% accuracy through preprocessing and model evaluation.
Corizo
Built deep learning models for retinopathy detection (72% accuracy) and road lane detection (86% accuracy, 74% precision) to support diagnosis and safety. Analyzed datasets to identify product trends, enhanced data collection methods, and created visualizations presenting complex insights effectively.
SkillVertex
Built house price prediction model achieving 68% precision to inform investment decisions. Enhanced data accuracy by 86% through preprocessing. Created interactive stock market visualizations for improved data interpretation.
Bachelor of Technology
Used YOLOv5 to detect helmetless riders and capture number plates and faces. Future plan includes live detection enhancement.
Built RNN model using Librosa and pyAudioAnalysis, achieving 88% precision by capturing temporal audio patterns.
Implemented multiple models (CNN, DeepFace, Keras, TensorFlow) for image-based sentiment detection.
Developing intelligent chatbot using JarvisAI with NLP for handling user inquiries.
Created collaborative filtering model on MovieLens dataset, achieving 85% accuracy in predicting preferences.
Trained model to recognize 43 signs using Keras and PyQt5, achieving 87% training accuracy and 93% validation accuracy.
Automated attendance using OpenCV DNN to detect faces, extract embeddings, and map to student identities.
Combined Bi-LSTM and GAN with Keras and Scikit-learn to enhance climate data prediction and synthesis.
Udemy
Udemy
Udemy
Udemy
Simplilearn
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