elasticnet
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MATLAB library of gradient descent algorithms for sparse modeling: Version 1.0.3
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- MATLAB
Predict the profitability of potential coffee shop locations using SQL and Python. Combines data engineering with feature-rich regression modeling, visual analytics, and business insights to support data-driven site selection and retail decision-making.
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Analyze and predict daily productivity using SQL, machine learning, and psychology. This project combines behavioral data, circadian rhythm analysis, and ElasticNet regression to model focus, stress, and performance, transforming work patterns into actionable insights.
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- Python
Repository for the Recommender Systems Challenge 2020/2021 @ PoliMi
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- Python
OIKAN is a neuro-symbolic machine learning framework inspired by Kolmogorov-Arnold representation theorem. It combines the power of modern neural networks with techniques for extracting clear, interpretable symbolic formulas from data. OIKAN is designed to make machine learning models both accurate and Interpretable.
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In this project, I applied different regression models for rmse and mae on antenna dataset for predict signal strength.
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Analysis of NBA player stats and salaries of the 2016-17 for the 17-18 season
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- Python
Code for paper "面向物联网隐私数据分析的分布式弹性网络回归学习算法"
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- MATLAB
SaleFore AI: Ultra-accurate sales forecasting using ensemble ML (XGBoost, LightGBM, CatBoost) with RTX 4060 GPU optimization. Achieves 88-95% accuracy with advanced hyperparameter tuning.
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- Python
Predicting the Residential Energy Usage across 113.6 million U.S. households using Machine Learning Algorithms (Regression and Ensemble)
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Solution Paths of Sparse Linear Support Vector Machine with Lasso or ELastic-Net Regularization
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📈 Stock Price Prediction using ElasticNetCV | 🥈 Dacon Top 5%
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- Python
Using publicly available data for the national factors that impact supply and demand of homes in US, build a data science model to study the effect of these variables on home prices.
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A repository containing the code for a machine learning project about predicting the outcome of soccer matches, including a simple web application built with Flask.
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The objective of the competition was to create the best recommender system for a book recommendation service by providing 10 recommended books to each user. The evaluation metric was MAP@10.
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- Python
Model that uses 10 different algorithms to predict the revenue of a movie before it's release
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- Jupyter Notebook
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