Arpan Pramanik
Arpan Pramanik
Explore my deployed Machine Learning projects by clicking on the "Projects" link above. Each project is interactive, allowing you to test models like car price prediction and calories burnt estimation etc. Dive in to experience the practical applications of AI and ML!
Get accurate price estimates for cars based on key features like brand, model, mileage, fuel type, and more. This project leverages machine learning to provide quick and reliable insights, helping users make smarter buying or selling decisions.
Accurately estimate calories burnt based on factors like age, weight, duration, activity type, and intensity. This project utilizes machine learning to deliver fast and reliable insights, helping users track and optimize their fitness goals effectively
Predict the likelihood of diabetes based on key health metrics like glucose level, blood pressure, age, and more. This project uses machine learning to provide quick and accurate predictions, supporting better health management and early intervention.
Determine the likelihood of loan approval based on factors like applicant income, credit history, loan amount, and more. This project leverages machine learning to provide fast and reliable predictions, aiding financial decision-making.
Predict the cost of medical insurance based on key factors such as age, BMI, smoking habits, and more. This project uses machine learning to provide accurate cost estimates, helping users plan their healthcare expenses effectively.
Analyze movie reviews to determine whether they express positive or negative sentiments. This project leverages deep learning techniques, particularly LSTM models, to accurately classify sentiment based on textual data. Gain insights into audience opinions and improve decision-making in areas like marketing, content strategy, and user experience.
The Smart Waste Classification system uses MobileNet to identify waste categories from images. Trained on the TrashNet dataset, it helps users determine if an item is recyclable, compostable, or non-recyclable. Along with classification, it provides recycling suggestions to promote sustainable waste management and environmental awareness through AI-powered insights.
The Fruits & Vegetables Classification system uses a CNN model to identify different fruits and vegetables from images. Trained on a custom dataset, it provides users with predictions and confidence scores. Built with TensorFlow and deployed using Streamlit, this tool offers a simple, interactive interface to upload images and view results, helping users identify produce accurately while visualizing the classification probabilities.
I am an enthusiastic AIML student currently pursuing a B.Tech in Computer Science and Engineering with a specialization in Artificial Intelligence and Machine Learning at The Neotia University. My passion lies in exploring the fascinating world of Machine Learning and Deep Learning, where I have worked on several impactful projects, including those deployed on platforms like Render. In addition to my work in AI, I am delving into the creative realm of Frontend Development, utilizing technologies like HTML, CSS, and Flask to bring my ideas to life. This website serves as a hub to showcase my projects, share my learning journey, and connect with like-minded individuals. Explore my work, and let’s build something amazing together!