I can analyse big data, construct predictive models, and translate data into meaningful insights that drive business strategy.

Results-driven professional with a passion for bringing ideas to life and constructing tangible solutions. Recognised for a keen interest in building and transforming conceptual visions into real world applications.

🤝 Collaboration Requests: [email protected]

⚙️ Machine Learning Library Nalyst

pip install nalyst

pip install --upgrade nalyst

https://pypi.org/project/nalyst/

🔑 A better future through meaningful insights! Join me on this exciting journey of data exploration and analysis. Together, we can make a difference.

• Data Analysis and Data Mining
• Data Preprocessing
• Statistical Analysis
• Data Visualization
• Advanced Mathematics
• Machine Learning (Logistic Regression, Linear Regression, Decision Trees, Random Forest, Support Vector Machines, Naïve Bayes, Neural Networks, Gradient Boosting)
• Deep Learning (Feedforward neural network, Convolutional Neural Networks, Recurrent Neural Networks and Autoencoders)
• Clustering Techniques (Hierarchical Clustering, DBSCAN)
• Dimensionality Reduction (PCA, SVD, Autoencoders)
• Time Series Analysis
• Dashboard and KPI Development
• Business Intelligence Tools (Microsoft Power BI, Tableau)
• Natural Language Processing (NLP)
• Text Mining
• A/B Testing
• Model Deployment
• Data Governance and Data Quality

• Technical and Fundamental Analysis
• Portfolio Construction and Management
• Risk Analysis
• Quantitative Analysis
• Trend Analysis (Linear Regression Model)
• Start-up Valuation
• Equity and Fixed Income Analysis
• Alternative Investments
• Option Pricing and Strategies
• Investment Strategy Development
• Financial Modeling and Forecasting
• Economic and Market Analysis
• Asset Allocation and Diversification
• Regulatory Compliance and Reporting

• Programming Languages: Python, JavaScript, C++ and R Programming
• Web Development & Framework: HTML, CSS & Streamlit, Node.js, Django, and React
• Database: Microsoft SQL, SQL Lite, and Mongodb
• Data Visualisation Tools: Power BI, Tableau, Matplotlib, and Seaborn
• Data Science Libraries (Api): Numpy, Pandas, Polars, Scikit-learn, Keras, TensorFlow, PyTorch, yfinance, pyttsx3, gTTS (Google Text-to-Speech), sounddevice, and SpeechRecognition
• Development Environments: Jupyter Notebook, Visual Studio Code, and Google Colab
• Microsoft Office Suite & Analytics: MS Word, MS Excel, MS Visio, MS PowerPoint, MS Team, and Google Analytics
• Collaborative Tools: Google Sheets
• Graphic Design: Adobe Photoshop

Talks about #statistics, #datascience, #machinelearning, #quantitativefinance, and #artificialintelligence