Abdul Shaikh

• Developed AI-driven Q&A chatbot systems using GPT-3.5 for efficient data extraction.
• Automated business processes using Power Automate and optimized workflows with Azure Machine Learning.
• Built financial report summarization systems with GPT-3.5 Turbo and Streamlit, enhancing data processing accuracy.

TECHNOLOGIES

Python, Streamlit, GPT-3.5, BERT, Claude model, CatBoost, Logistic Regression, KNN, K-means, Decision Trees, Random Forest, Azure Machine Learning Studio, Azure Open AI, Power Automate, Azure Cloud Storage, GPT-3.5, Claude model, Azure Cloud Storage, Jupyter, Streamlit, Power Automate

• Developed a machine learning model (CatBoost) for predicting employee attrition.
• Applied sentiment analysis using BERT and Flant5 to predict employee retention.

TECHNOLOGIES

Python, Jupyter, CatBoost, Logistic Regression, KNN, K-means, Decision Trees, Random Forest, LSTM, BERT, Flant5, Streamlit

• Created demand forecasting models using ARIMA, LSTM, and DARTS for supply chain optimization.
• Implemented precise sales forecasts across multiple markets, improving business decision-making

TECHNOLOGIES

Python, Flask, ARMA, ARIMA, ARIMAX, SARIMA, SARIMAX, LSTM, DARTS, RNN, MAPE, Jupyter Notebook, Flask Framework

• Developed a machine learning-based demand forecasting model covering over 400 stores and 500 stock-keeping units (SKUs).
• Used Prophet, Random Forest, and LightGBM to build an ensemble model for accurate demand forecasting.
• Trained models using time-series data, analyzing the impact of factors like holidays, promotions, and events on demand forecasts.
• Profiled and disaggregated daily demand from weekly forecasts for various product categories.
• Evaluated model performance and made necessary adjustments to improve forecast accuracy.

TECHNOLOGIES

Python, Jupyter, Prophet, Random Forest, LightGBM, Time-Series Data Forecasting, Meta-Learning, Ensemble Models

• Implemented end-to-end demand forecasting and sensing pipelines for Loreal’s eCommerce and offline channels.
• Optimized the supply chain by integrating forecasting with inventory management.
• Created forecasts for new product introductions by identifying key features and using machine learning models.
• Utilized Sktime, DARTS, and Pyflux to predict demand for products across different channels.

TECHNOLOGIES

Python, Jupyter, Sktime, DARTS, Pyflux, Jupyter Notebook

• Developed a model to extract skill sets from a resume repository and generate profile summaries based on skillset, experience, and projects.
• Scored resumes by analyzing extracted information to provide precise profile assessments.
• Utilized NLP and LinkedIn Scraper (Phantombuster) to gather and process relevant data from resumes.
• Streamlined resume scoring and grading for more accurate talent matching.

TECHNOLOGIES

Python, Jupyter, NLP, Phantombuster (LinkedIn Scraper), Python, Jupyter Notebook, LinkedIn Scraper