Machine Learnig Based Projects
"With our machine learning project services, you can unlock actionable insights from your data, automate decision-making processes, and stay ahead of the competition in today's data-driven world. Our team of experienced data scientists and engineers specializes in creating custom machine learning algorithms and models that solve complex business problems, whether it's predictive analytics, natural language processing, computer vision, or recommendation systems."
Real-Time Student Engagement Detection in e-learning environments using emotion analysis, and eye tracking
Develop a system to detect the engagement level of the students using the information about the movements of the eyes and head, and facial emotions. Classify the engagement level into three categories: “very engaged”, “nominally engaged” and “not engaged at all”.
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Face Recognition
Develop a face recognition system for security and identification purposes.
Collect a dataset of labeled images of faces and preprocess them using techniques like alignment and normalization. Train models using deep learning techniques such as CNNs or FaceNet. Evaluate the model using accuracy, precision, recall, and ROC-AUC. Deploy the system for real-time face identification in applications like attendance systems or access control.
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House Price Prediction
Build a regression model to predict house prices based on features like location, size, and amenities.
Use datasets such as the Boston Housing dataset, preprocess the data, and engineer relevant features. Train models like linear regression, decision trees, or gradient boosting, and evaluate using RMSE, MAE, and R-squared.
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Image Classification with Convolutional Neural Networks (CNNs)
Develop a model to classify images into various categories, such as animals, objects, or scenes.
The project involves collecting a dataset, preprocessing images, and building a CNN using frameworks like TensorFlow or PyTorch. Evaluate the model's performance using metrics like accuracy and confusion matrix, and implement techniques like data augmentation to improve accuracy.
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Recommendation System for E-Commerce
Develop a recommendation system to suggest products to users based on their browsing and purchasing history.
Use collaborative filtering, content-based filtering, or hybrid methods to build the model. Evaluate the system using metrics like precision, recall, and Mean Average Precision (MAP), and deploy it to a mock e-commerce website for real-time recommendations.
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Speech Recognition System
Build a model to transcribe spoken language into text.
Collect audio datasets and preprocess them by converting to spectrograms. Use recurrent neural networks (RNNs) or transformer models to train the speech-to-text system. Evaluate the model using Word Error Rate (WER) and deploy it as a real-time application.
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AI-Powered Chatbot
Develop an intelligent chatbot for customer support using NLP and machine learning.
Use datasets of customer queries and responses to train models such as seq2seq, transformer-based models, or rule-based systems. Evaluate using metrics like response accuracy and user satisfaction, and deploy the chatbot on platforms like websites or messaging apps.
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