My Data Voyage
Data Scientist | Machine Learning & NLP Enthusiast | Insight Generator
Featured Project
Sentiment Analysis
Developed an NLP solution to analyse customer feedback sentiment. Implemented text preprocessing techniques and trained a BERT-based model to classify sentiment with 92% accuracy. Technologies: Python, NLTK, Transformers, PyTorch.
Technologies: Python, NLTK, Transformers, PyTorch.
Project Portfolio
Customer Segmentation
Analysed a dataset through exploration and preprocessing, conducted feature engineering, determined the optimal number of clusters (k), and applied machine learning models to segment customers effectively.
Technologies: Python, Scikit-learn, Pandas, Clustering Algorithms
Student Dropout Prediction
Conducted phased data exploration, preprocessing, and feature engineering. Built and compared predictive models using XGBoost and a neural network to forecast student dropout rates with high accuracy.
Technologies: Python, XGBoost, TensorFlow, Pandas
Statistical Hypothesis Testing
Applied statistical hypothesis testing to evaluate organisational data scenarios. Explored the differences between correlation and causation in data analysis.
Technologies: Python, Statistical Methods
Anomaly Detection
Explored a dataset to identify patterns, preprocessed data, and performed feature engineering. Applied statistical techniques and machine learning algorithms to detect anomalies, followed by a detailed report summarising findings and recommendations.
Technologies: Python, Pandas, Scikit-learn, Statistical Methods
Time Series Forecasting
Analysed historical sales data using time series decomposition, feature engineering, and ARIMA modeling to forecast future demand. Achieved 15% improvement in forecast accuracy over baseline methods.
Technologies: Python, Statsmodels, Prophet, Pandas
Neural Network Project
Designed and implemented a deep neural network architecture from scratch. Applied forward and backward propagation algorithms, optimized hyperparameters, and achieved state-of-the-art performance on classification tasks.
Technologies: Python, TensorFlow, Keras, NumPy, Matplotlib
Explore More
Movie Review Sentiment Classification System
Advanced Neural Network Architecture Visualisation
Interactive Neural Network Learning Demonstrator
Hyperparameter Optimisation Visual Analytics
Foundation Neural Network Model Explorer
Deep Learning Network Implementation Framework
Automated Hyperparameter Tuning Pipeline
Comprehensive Model Evaluation Metrics Suite
Gradient Descent Optimiser Comparative Analysis
Titanic Survival Prediction Neural Network
Supervised Learning Algorithm Implementation
Advanced Time Series Forecasting Models
Customer Behavioural Segmentation Analytics
Maritime Engine Anomaly Detection System
Statistical Hypothesis Testing Framework
Student Retention Predictive Analytics
Advanced NLP Sentiment Classification Engine
Custom Deep Learning Architecture Design
Technical Skills
Deep Learning & Neural Networks
Custom neural network architectures • Forward/backward propagation • Gradient descent optimisation • TensorFlow, Keras, PyTorch
Natural Language Processing
Sentiment analysis (92%+ accuracy) • BERT implementation • Text classification • NLTK, spaCy, Hugging Face Transformers
Machine Learning Engineering
Supervised/unsupervised learning • XGBoost, Random Forests, SVM • K-means, DBSCAN, Hierarchical clustering
Model Optimisation & Evaluation
Hyperparameter tuning • Grid/Random/Bayesian search • ROC-AUC, precision-recall • Custom business metrics
Time Series Analysis & Forecasting
ARIMA, SARIMA, Prophet, LSTM • Decomposition techniques • 15%+ accuracy improvement • Demand forecasting
Anomaly Detection Systems
Real-time detection (95%+ accuracy) • Statistical methods, Isolation Forests • Autoencoders • Maritime systems
Data Visualisation & Analytics
Interactive dashboards • Matplotlib, Seaborn, Plotly, D3.js • SHAP values • Business intelligence reporting
Feature Engineering & Dimensionality Reduction
Feature creation/selection • PCA, t-SNE, UMAP • Autoencoders • High-dimensional data processing
Python & Data Science Stack
Production-ready ML pipelines • NumPy, Pandas, Scikit-learn • Automated workflows • Scalable systems
Statistical Analysis & Hypothesis Testing
Parametric/non-parametric tests • A/B testing • Correlation analysis • Causal inference • Model validation
MLOps & Model Deployment
Model versioning • Experiment tracking • Deployment pipelines • Drift detection • Automated retraining
Customer Analytics & Segmentation
Behavioural segmentation • RFM analysis • Cohort analysis • Targeted marketing • Retention optimisation
Visualisation Gallery
A selection of my data visualisation techniques
Contact Me
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Location
Based in London, UK