Pinned Repositories
bbiaggi88
Config files for my GitHub profile.
customer_segmentation
This project applies unsupervised learning to segment customers in a grocery database. By analyzing income, spending, and family data, it optimizes marketing strategies for tailored product offerings.
house_price_prediction
This project aims to predict house prices using machine learning. Analyzing a dataset with a bunch of different features, different models were used to estimate prices accurately. Regression algorithms like Linear, Ridge, Lasso, ElasticNet, Random Forest, SVR, and XGBoost are evaluated for performance.
recommendation_system
This project is a music recommendation system that leverages machine learning techniques to recommend songs based on user input. The system utilizes a dataset of songs with various attributes such as valence, danceability, and energy to provide personalized recommendations.
sms_spam_detection
The SMS Spam Detection project aimed to develop an efficient machine learning model to classify SMS messages as either "ham" (non-spam) or "spam". The project involved several key steps, including data cleaning, and text preprocessing techniques were applied to transform the raw text data into a format suitable for analysis and modeling.
bbiaggi88's Repositories
bbiaggi88/bbiaggi88
Config files for my GitHub profile.
bbiaggi88/customer_segmentation
This project applies unsupervised learning to segment customers in a grocery database. By analyzing income, spending, and family data, it optimizes marketing strategies for tailored product offerings.
bbiaggi88/house_price_prediction
This project aims to predict house prices using machine learning. Analyzing a dataset with a bunch of different features, different models were used to estimate prices accurately. Regression algorithms like Linear, Ridge, Lasso, ElasticNet, Random Forest, SVR, and XGBoost are evaluated for performance.
bbiaggi88/recommendation_system
This project is a music recommendation system that leverages machine learning techniques to recommend songs based on user input. The system utilizes a dataset of songs with various attributes such as valence, danceability, and energy to provide personalized recommendations.
bbiaggi88/sms_spam_detection
The SMS Spam Detection project aimed to develop an efficient machine learning model to classify SMS messages as either "ham" (non-spam) or "spam". The project involved several key steps, including data cleaning, and text preprocessing techniques were applied to transform the raw text data into a format suitable for analysis and modeling.