📊 Data Analysis and Interpretation: Expert in data cleaning, transformation, and exploratory data analysis (EDA) employing Python, Pandas, and SQL to derive actionable insights from complex datasets.

🤖 Machine Learning Expertise: Proficient in the development and implementation of sophisticated machine learning algorithms, including: -Regression Techniques: Mastery of linear regression, logistic regression, and advanced regularisation techniques (Lasso, Ridge). -Classification Models: Skilled in deploying decision trees, random forests, support vector machines (SVM), and boosting methods (XGBoost, LightGBM) for predictive analytics. -Clustering and Segmentation: Experienced in K-means clustering, hierarchical clustering, and DBSCAN for market segmentation analysis. -Dimensionality Reduction: Proficient in PCA (Principal Component Analysis) and t-SNE for data visualisation and feature extraction.

📈 Advanced Statistical Analysis: Strong foundation in inferential statistics, hypothesis testing, and A/B testing, enabling robust data-driven decision-making in marketing and finance.

🧠 Deep Learning Applications: Experienced in designing and implementing neural networks using TensorFlow and Keras, including: -Convolutional Neural Networks (CNNs) for image processing tasks. -Recurrent Neural Networks (RNNs) and LSTMs (Long Short-Term Memory) for time series forecasting and natural language processing (NLP) applications. 📝 Natural Language Processing (NLP): Proficient in text analysis, sentiment evaluation, and language modelling to inform marketing strategies.

🎨 Data Visualisation and Communication: Skilled in utilising tools such as Matplotlib, Seaborn, and Tableau to create compelling visual representations of data, facilitating effective stakeholder communication.

💹 Financial Modelling and Analysis: Strong ability to construct financial models to evaluate business performance, profitability, and risk assessment.

🔍 Market Research and Consumer Insights:: Expertise in employing data analytics to uncover consumer behaviours, trends, and preferences, informing strategic marketing initiatives.

🌐 Big Data Technologies: Familiarity with Apache Spark and Hadoop for efficient processing and analysis of large-scale datasets.

🚀 Model Deployment and Optimisation: Experience in deploying machine learning models using Flask or FastAPI, with a strong emphasis on hyperparameter tuning and model performance enhancement.

🗄️ Database Management: Proficient in SQL and NoSQL databases, ensuring effective data storage and retrieval strategies.

💡 Critical Thinking and Problem-Solving: Exceptional analytical and logical reasoning skills, enabling the synthesis of complex data into meaningful insights.

🤝 Collaborative Communication: Excellent interpersonal skills, adept at conveying technical information to non-technical stakeholders in a clear and concise manner.

• Financial Modeling & Analysis
• Time Series Analysis
• Risk Management Techniques
• Market Research & Consumer Behavior Analysis
• Predictive Analytics

• Analytical Thinking
• Problem Solving
• Communication Skills
• Team Collaboration
• Project Management

PROJECTS:

In the Sentiment Insights: Analyzing ChatGPT User Reviews project, I employed natural language processing (NLP) techniques to evaluate user feedback on ChatGPT. Utilizing Python libraries like VADER for sentiment analysis, I categorized reviews into positive, neutral, and negative sentiments. Data preprocessing techniques such as tokenization and stopword removal were implemented, and the results were visualized with Plotly. This project showcases my expertise in sentiment analysis and data visualization, yielding actionable insights based on user feedback trends.

In the EA Stocks Financial Analysis with Prophet and Plotly project, I performed a time series analysis using Facebook Prophet to forecast EA stock trends. I integrated Python and Plotly for interactive financial visualizations, highlighting stock price movement and patterns. The project involved data preprocessing, trend analysis, and creating advanced visual representations to aid decision-making in financial markets.

In S&P 500 Stocks Time Series Regression project, I conducted a time series regression analysis on S&P 500 stock data to identify trends and patterns affecting stock prices. Utilizing advanced statistical techniques and machine learning models, I forecasted future stock performance and visualized the results for clear interpretation. This approach provided valuable insights for investment decisions.

In Loan Risk Forecasting: ML and Financial Analysis project, I utilized advanced machine learning techniques, including Random Forest and XGBoost, for loan status prediction. I implemented SHAP for interpretability, allowing insights into model predictions. Hyperparameter tuning was conducted to optimize model performance, assessed using ROC and AUC metrics. Additionally, I integrated financial analysis to enhance the predictive capability and relevance of the models within a financial context.