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: analyticalharry@gmail.com
⚙️ 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
