- A dedicated Data Scientist with a strong background in statistical analysis, machine learning, data visualization, and biomedical research
- Proficient in python programming language
- Proven track record of developing predictive models
- Strong problem-solving skills and a passion for continuous learning and professional development
Bachelor's degree (Honors – First class) – Medical Laboratory Science – Histopathology and Cytology Department
An emotion analysis NLP data science project involves utilizing Natural Language Processing (NLP) techniques to analyze and understand emotions expressed in textual data. By applying sentiment analysis, text classification, and other NLP methods, this project aims to extract and interpret emotional cues, sentiments, and tones present in the text. Such project's outcomes can be applied in various fields such as market research, social media analysis, customer feedback analysis, mental health assessment, and sentiment monitoring. The insights gained from emotion analysis using NLP can help organizations enhance customer experience, tailor marketing strategies, improve mental health support systems, and gain a better understanding of public sentiment towards products, services, or events.
Algorthms: Bidirectional GRU
This project involves examining various health indicators and their relationship to diabetes. This project typically includes analyzing data related to factors such as blood glucose levels, , BMI, blood pressure, cholesterol levels, family history of diabetes, lifestyle habits, and more. The goal of the project is to identify patterns, correlations, and predictive factors that may influence the risk of developing diabetes or the progression of the disease. By applying data science techniques such as data preprocessing, exploratory data analysis, feature engineering, machine learning modeling, and statistical analysis.
Algorthms: RFC, f1 score(0.92, 0.95)
Forecasting Apple stock prices involves utilizing historical data, economic indicators, and industry trends to predict the future movements of the company's shares. Using techniques such as machine learning, deep neural networks, and time series analysis, analysts can leverage past stock performance, financial reports, and market events to generate predictive models. While stock price predictions do not guarantee absolute accuracy, they provide valuable insights for investors to make informed decisions. By employing analytical tools and key influencing factors, predicting Apple stock prices can help anticipate potential fluctuations in the market.
Algorithm: LSTM, GRU
Result: GRU, RMSE = 2.41
In this project, Natural Language Processing (NLP) techniques are employed to analyze and derive insights from customer reviews and feedback related to Singapore Airlines services. The project focuses on processing and understanding the sentiments, opinions, and experiences shared by passengers through written reviews. Using sentiment analysis and topic modeling, valuable information were extracted from the text data to identify recurring themes and sentiments. By categorizing reviews based on positive, negative, or neutral sentiments, the project aims to gauge customer satisfaction levels and highlight specific aspects of the airline's services that are praised or criticized. The outcomes of this analysis can help Singapore Airlines to gain a comprehensive understanding of customer perceptions, identify trends in feedback, and make data-driven decisions to enhance their services, improve customer satisfaction, and maintain a competitive edge in the airline industry. Additionally, insights from this project can be valuable for marketing strategies, operational improvements, and customer experience enhancement initiatives.
Algorthm: Bidirectional LSTM
- Tumors Benign\Malignant Classification (Accuracy: 99%, F1 Score (0.98, 0.99)
- Cardiovascular Disease Prediction (Accuracy: 98%, F1 Score (0.98, 0.99)
- Obesity Level Prediction (Accuracy: 91%, F1 Score (0.91, 0.89)
- Adults Income Prediction (Accuracy: 82.5%, F1 Score (0.82, 0.82)
- Cervical Cancer Prediction (Accuracy: 95%, F1 Score (0.97, 0.69)
- Credit Card Fraud Detection (Accuracy: 93%, F1 Score (0.96, 0.97)
- Diabetes Health Indicators (Accuracy: 94%, F1 Score (0.92, 0.95)
- Human Activity Recognition Using Smart Phone Data (Accuracy: 98%, F1 Score (1 – 0.96)
- Students Stress Levels Classification (Accuracy: 90%, F1 Score (0.87 – 0.85)
- General Health Prediction: NHANES 2009–2012 dataset (Accuracy: 75%, F1 Score (0.82 – 0.68)
- US Health Insurance Charges Prediction (R2: 0.83, MSE: 0.303)
- House Price Prediction (R2: 0.88, MSE: 0.01)
- Life Expectancy Prediction (R2: 0.90, MSE: 13.4)
- Melbourne House Price Prediction (R2: 0.866, MSE: 0.0012)
- Emotions Analysis: Bidirectional GRU
- Singapore Airlines Reviews: Bidirectional LSTM
- Mental Health Dataset Analysis: RSF, MultinomialNB
- Disease Prediction: ANN
- Emotions Analysis: Bidirectional GRU
- Singapore Airlines Reviews: Bidirectional LSTM
- Apple Stock Price Prediction LSTM, GRU
RESEARCH PROJECTS