Hello
Here, you’ll find a collection of my previous projects, each showcasing my expertise in transforming complex challenges into actionable outcomes. I invite you to explore these projects and how I can apply the same expertise to your ideas. Take a look around, and let's explore how we can create something remarkable together!
Skin Disease Prediction
Objective: Develop a Image-Based Diagnostic Tool for predicting skin diseases.
Key Implementations:
-
Designed and implemented a Convolutional Neural Network (CNN) model to predict skin diseases.
-
Enabled real-time diagnostic assistance through image uploads.
-
Utilized data augmentation methods to enhance the model's prediction accuracy.
Outcome: Enhanced the accuracy and speed of skin disease diagnostics.
PCOD Risk Prediction Model
Objective: Develop a machine learning model to predict the likelihood of PCOD based on patient-entered details.
Key Implementations:
-
Created a predictive model using Gradient Boosting Machines (GBM) to evaluate the risk of PCOD.
-
Utilized a comprehensive dataset of 700,000 records, encompassing diverse women across various ages, professions, cultures, and regions, with data on different physical and medical aspects.
-
Designed the tool to analyze basic patient information to estimate the probability of PCOD, guiding individuals to seek medical consultation if the risk is identified.
Outcome: Enabled patients to receive an initial assessment of their PCOD risk from simple input details, facilitating early medical advice and intervention.
Course Recommendation System
Objective: Develop a personalized course recommendation system for a learning platform using machine learning.
Key Implementations:
-
Built a recommendation system leveraging a BERT model to suggest courses to candidates.
-
The system ranks course recommendations by 1st, 2nd, and 3rd priority based on the candidate’s previous searches, registered courses, and progression toward completing previously registered courses.
-
Tailored recommendations to enhance user engagement and learning outcomes by aligning course suggestions with individual learning paths.
Outcome: Provided candidates with a personalized learning experience by recommending the most relevant courses, improving course completion rates and overall user satisfaction.