Transform your communication experience with a custom Skype bot built in Python, deployed on AWS, and designed to integrate with advanced LLM services. This project unlocks the potential of real-time conversations enhanced by powerful language models, offering seamless automation and intelligent responses.
🚀 Project Highlights & Key Points
✅ Big Data Processing: Managed a large Parquet dataset efficiently with Dask, enabling batch processing and parallel computation.
✅ Geospatial Enrichment: Integrated external POI data (malls, banks, universities, schools) to provide additional context for cost-of-living estimations.
✅ Handling Missing Data: Used KNN Imputer to estimate missing values rather than discarding incomplete records.
✅ Robust Machine Learning Model: Built a Random Forest Regression model to predict cost-of-living variations within H8 hexagons.
How long will a user stay tuned? This project dives into predicting podcast listening time using XGBoost, with a strong focus on preprocessing challenges. From crafting meaningful features to handling missing values and vectorizing categorical variables, every step was designed to boost predictive accuracy. Experiment tracking was powered by Comet ML, ensuring full transparency and reproducibility throughout the modeling process.
Highlights:
Engineered features from session data, user behavior, and content metadata
Imputed missing values and vectorized categorical variables for model readiness
Leveraged XGBoost for powerful, scalable regression performance
Tracked experiments and model metrics seamlessly with Comet ML
A great example of applied ML in media analytics—turning raw data into predictive insights! 🎧📊
Discover how to predict house prices with precision using PySpark's robust data processing capabilities and MLflow for seamless model tracking. This project dives into scalable machine learning, leveraging PySpark for regression analysis and MLflow to log metrics, monitor performance, and streamline experimentation. Perfect for those exploring big data ML pipelines! 🚀
Highlights:
Scalable regression modeling with PySpark
Real-time performance tracking using MLflow
Optimized for large datasets and practical insights
Unleash the power of big data for smarter predictions!
Organizing messy product catalogs just got easier! In this machine learning project, I used a Random Forest model to classify products into macro categories based on their names. The biggest challenge? Cleaning and standardizing noisy product names—often filled with typos, special characters, and inconsistent formats. With robust preprocessing and cross-validation for hyperparameter tuning, the model achieved accurate and scalable categorization.
Highlights:
Cleaned and normalized messy product names for reliable feature extraction
Used Random Forest for multi-class classification with strong interpretability
Applied cross-validation to fine-tune model parameters and avoid overfitting
Delivered an automated solution to streamline product tagging and organization
Ideal for anyone looking to bring structure to unorganized product data using machine learning! 🛍️🌲
Harness the power of big data with this end-to-end machine learning project! Using PySpark. With MLflow, I streamlined experiment tracking, ensuring reproducibility and performance monitoring. A custom function handled categorical features with one-hot encoding, making preprocessing efficient and scalable.
Highlights:
Managed imbalanced data with resampling techniques
Removed duplicates and engineered features
Built a dynamic one-hot encoding function for categorical handling
Tracked metrics and optimized hyperparameters using MLflow
Perfect for those looking to scale ML models with PySpark while maintaining experiment traceability! 🚀
Maximize customer profitability while minimizing ad spend! This project dives deep into customer segmentation for an e-commerce business, leveraging data analysis to uncover key purchasing patterns. Using logistic regression, we predict which customers are most likely to convert with fewer ad impressions, optimizing marketing efforts for efficiency and ROI.
Highlights:
Performed in-depth customer segmentation to identify high-value shoppers
Analyzed purchase behavior and ad engagement to refine marketing strategies
Built a logistic regression model to predict profitable customers with minimal ad exposure
A must-read for marketers and data scientists looking to enhance customer targeting with machine learning! 🚀
Discover how machine learning transforms e-commerce strategy! This project explores customer segmentation using K-Means and DBSCAN clustering, identifying high-value shoppers based on purchasing behavior. By comparing different clustering techniques, we uncover the most effective way to group customers for targeted marketing and higher profitability.
Highlights:
Applied K-Means and DBSCAN to segment customers based on shopping patterns
Identified high-value groups for personalized marketing and increased ROI
Compared clustering methods to find the best approach for e-commerce segmentation
Delivered actionable insights to optimize product recommendations and ad spend