#Predictive-modeling
Showing 55 of 55 repositories tagged #predictive-modeling, ranked by stars
An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
A Julia machine learning framework
Machine Learning in R
moDel Agnostic Language for Exploration and eXplanation
SMT: The Surrogate Modeling Toolbox
Retentioneering: product analytics, data-driven CJM optimization, marketing analytics, web analytics, transaction analytics, graph visualization, process mining, and behavioral segmentation in Python. Predictive analytics over clickstream, AB tests, machine learning, and Markov Chain simulations.
OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research
Keras implementation of Representation Learning with Contrastive Predictive Coding
Fast, high-quality forecasts on relational and multivariate time-series data powered by new feature learning algorithms and automated ML.
Analytics & Machine Learning R Sidekick
An open source book to learn data science, data analysis and machine learning, suitable for all ages!
A micro neural network multilayer perceptron for MicroPython (used on ESP32 and Pycom modules)
⚽ High-performance football analytics: build data pipelines, scrape data, model matches, rank teams, and bet smarter | Powered by www.pena.lt/y 🚀
SF Brigade's Data Science Working Group.
AI-powered NBA game outcome predictor that uses advanced team stats and trend-based features to forecast winners and track model performance
Website sources for Applied Machine Learning for Tabular Data
Top Dynamic AI World Simulation & Storytelling Tools 2026
Code for the CUP Elements on text analysis in Python for social scientists
Predicts Daily NBA Games Using a Logistic Regression Model
Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks
Developer Version of the R package CAST: Caret Applications for Spatio-Temporal models
The Google Advanced Data Analytics Certificate contains information on how to use machine learning, predictive modeling, and experimental design to collect and analyze large amounts of data, and prepare for jobs like Senior Data Analyst and Junior Data Scientist.
A python multi-variate time series prediction library working with sklearn
Prediction of Solar Power Generated by a power plant using artificial neural networks
🚀 Professional AI-powered stock market dashboard with real-time technical analysis, machine learning price predictions, and intelligent market insights. Built with Python, Streamlit, and scikit-learn.
A collection of machine learning projects
Personal repository of data science demonstrations and references
AIFootballPredictions is an ML-based system to predict if a football match will have over 2.5 goals. Using historical data from top European leagues (Serie A, EPL, Bundesliga, La Liga, Ligue 1), it employs advanced feature engineering and model training techniques to provide accurate predictions. Perfect for sports analytics enthusiasts.
In Stock Market Prediction, our aim is to build an efficient Machine Learning model to predict the future value of the financial stocks of a company.Our machine learning model will be presented to retail investors with a third-party web app with the help of Streamlit.
This repository provides the code examples for the corresponding blog posts. In case you have questions, feel free to contact me directly.
Lectures for Introduction to Data Science for Public Policy (PPOL 670-01)
This repository contains the pytorch code for the 2023 ICASSP paper "Preformer: Predictive Transformer with Multi-Scale Segment-wise Correlations for Long-Term Time Series Forecasting”
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.
Predict stock prices using LSTM networks in PyTorch. This project covers data preprocessing, sliding window creation, model training with early stopping, and evaluation with RMSE/MAE/MAPE. Includes visualizations of training loss, predicted vs actual prices, and short-horizon forecasts.
Crypto AI Analytics is a sophisticated desktop tool designed for data-driven crypto market analysis and intelligent trend detection.
Kaggle Kernels (Python, R, Jupyter Notebooks)
Modelling real estate market in Budapest using machine learning
Signal Processing Toolkit, including ML models with visualization
PyTorch implementation of LSTM Neural Network for Multi-time-horizon solar forecasting
Curso diseñado para proporcionar una comprensión muy profunda del Trading Cuantitativo, fusionando los principios de Ingeniería Financiera con el poder de la Inteligencia Artificial, todo implementado en Python. Desarrollarás algoritmos y estrategias avanzadas que aprovechan datos financieros y técnicas de Inteligencia Artificial.
This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.
Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity
An advanced machine learning model utilizes a Random Forest Regressor to generate betting recommendations for Major League Baseball (MLB) games.
A collection of machine learning projects in Python, showcasing algorithms for classification, regression, and clustering using libraries like Scikit-learn and Pandas. Includes datasets, code, and tutorials for practical applications in data analysis and predictive modeling.
UFC bout winner prediction using neural nets.
Understand the relationships between various features in relation with the sale price of a house using exploratory data analysis and statistical analysis. Applied ML algorithms such as Multiple Linear Regression, Ridge Regression and Lasso Regression in combination with cross validation. Performed parameter tuning, compared the test scores and suggested a best model to predict the final sale price of a house. Seaborn is used to plot graphs and scikit learn package is used for statistical analysis.
🎯 Build a winning recommendation system with this effective generative framework, advancing to the finals of the 2025 Tencent Advertising Algorithm Competition.
Fraud Detection for VoIP. Use SentryPeer® HQ to help prevent VoIP cyberattacks and fraudulent VoIP phone calls (toll fraud) at https://sentrypeer.com
Statistical Modeling and Regression Analysis for Life Expentancy
📄 Enhance Apple Developer docs by converting them into AI-readable Markdown for easier access and improved usability.
Next-generation analytics & ML-powered churn prediction for Solana gaming. Self-training models predict player churn 14 days in advance. Live dashboard + REST API analyzing 60M+ on-chain transactions across 12 games.
A curated list of important projects, articles, papers, and other information concerning DeFi analytics.
Portfolio of course work for my Master's in Data Science.
[Python] A Jupyter notebook illustrating methods for analyzing a historical lottery results dataset. The example demonstrates assessing linear relationships between variables, incorporating astronomical data, and visualizing number distributions.