As an experienced Data Scientist, I am focused on enhancing energy systems through data-driven methods. I am an expert in managing large datasets, effective data cleansing, and advanced statistical analysis. I develop predictive machine learning models to forecast energy trends and support strategic decisions.
Core Competencies:
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Data Management: Competent in managing large-scale data sources, ensuring data integrity and accessibility.
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Data Cleansing and Transformation: Skilled in improving data quality by correcting inaccuracies and inconsistencies.
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Statistical Analysis: Expert in analyzing complex datasets and identifying significant patterns.
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Machine Learning Development: Proficient in creating predictive models using the latest techniques to improve operational efficiency and trend predictions.
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Data Visualization and Reporting: Talented in creating visuals and reports that effectively communicate data findings.
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Continuous Improvement (CI/CD): Committed to the continuous updating and maintaining the effectiveness of models and processes.
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Time Series Analysis: In-depth knowledge and experience in using time series data to analyze and predict solar energy production and consumption trends.
As a seasoned Data Scientist and AI Engineer, I am actively involved in mentoring and educating aspiring data professionals. My mentorship and training program covers a comprehensive range of topics in the field of Data Science and Machine Learning. Here's an overview of the key areas I focus on:
- Python Programming: Fundamental to advanced concepts in Python, tailored for data science applications.
- CRM Analytics: Techniques and insights for analyzing customer data, improving customer relationships and driving business growth.
- Measurement Problems in Data Science: Tackling common challenges in data measurement and ensuring data integrity.
- Recommendation Systems: Developing systems to suggest products or services to users based on their preferences and historical data.
- Feature Engineering: Techniques for creating and selecting impactful features to improve the performance of machine learning models.
- Machine Learning: Comprehensive understanding of various machine learning algorithms, from basic to advanced, including supervised and unsupervised learning.
This program is designed to empower participants with practical skills and in-depth knowledge, equipping them to excel in the rapidly evolving field of Data Science. For more information or to participate in my training sessions, feel free to contact me.
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Python Libraries: Pandas, Numpy, Seaborn, Sckit-Learn, SciPy, Matplotlib, TensorFlow, PyTorch, Scrapy
Machine Learning Algorithms : Linear Regression, Logistic Regression, Decision Trees, SVM, kNN, K-Means, Random Forest, GradientBoosting Algorithms(GBM, XGBoost,LightGBM, CatBoost)
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Predicting Service Request Loads using LSTM model with Keras
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Telco Churn Prediction with Machine Learning Models (%80 Accuracy)l