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The Top 339 Predictive Modeling Open Source Projects on Github
Categories
>
Machine Learning
>
Predictive Modeling
Islr Python
⭐
3,201
An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
Mlr
⭐
1,536
Machine Learning in R
Mlj.jl
⭐
1,229
A Julia machine learning framework
Skater
⭐
973
Python Library for Model Interpretation/Explanations
Dalex
⭐
945
moDel Agnostic Language for Exploration and eXplanation
Coursera Machine Learning
⭐
801
Coursera Machine Learning - Python code
Retentioneering Tools
⭐
409
Retentioneering: product analytics, data-driven customer journey map optimization, marketing analytics, web analytics, transaction analytics, graph visualization, and behavioral segmentation with customer segments in Python. Opensource analytics, predictive analytics over clickstream, sentiment analysis, AB tests, machine learning, and Monte Carlo Markov Chain simulations, extending Pandas, Networkx and sklearn.
Openchem
⭐
356
OpenChem: Deep Learning toolkit for Computational Chemistry and Drug Design Research
Pytorch Cortexnet
⭐
349
PyTorch implementation of the CortexNet predictive model
Smt
⭐
308
Surrogate Modeling Toolbox
Contrastive Predictive Coding
⭐
269
Keras implementation of Representation Learning with Contrastive Predictive Coding
Machine Learning Deployment
⭐
208
Launch machine learning models into production using flask, docker etc.
Modelstudio
⭐
206
📍 Interactive Studio for Explanatory Model Analysis
Timemachines
⭐
204
Continuously evaluated, functional, incremental, time-series forecasting
Data Science Live Book
⭐
177
An open source book to learn data science, data analysis and machine learning, suitable for all ages!
Data Science Wg
⭐
122
SF Brigade's Data Science Working Group.
Anndotnet
⭐
110
ANNdotNET - deep learning tool on .NET Platform.
Contrastive Predictive Coding Pytorch
⭐
109
Contrastive Predictive Coding for Automatic Speaker Verification
Micromlp
⭐
81
A micro neural network multilayer perceptron for MicroPython (used on ESP32 and Pycom modules)
Ml2017fall
⭐
63
Machine Learning (EE 5184) in NTU
Machineshop
⭐
54
MachineShop: R package of models and tools for machine learning
Cast
⭐
53
Developer Version of the R package CAST: Caret Applications for Spatio-Temporal models
Endtoend Predictive Modeling Using Python
⭐
48
Data Science Journal
⭐
46
Personal repository of data science demonstrations and references
Nba Predict
⭐
46
Predicts Daily NBA Games Using a Logistic Regression Model
Stock Return Prediction Using Knn Svm Guassian Process Adaboost Tree Regression And Qda
⭐
42
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
Predicting Myers Briggs Type Indicator With Recurrent Neural Networks
⭐
41
Data Science
⭐
39
Lectures for Introduction to Data Science for Public Policy (PPOL 670-01)
Firets
⭐
38
A python multi-variate time series prediction library working with sklearn
Mljar Api Python
⭐
38
A simple python wrapper over MLJAR API.
Bas
⭐
32
BAS R package https://merliseclyde.github.io/BAS/
Learnr
⭐
30
Exploratory, Inferential and Predictive data analysis. Feel free to show your ❤️ by giving a star ⭐️
Market Mix Modeling
⭐
26
Market Mix Modeling for an eCommerce firm to estimate the impact of various marketing levers on sales
Dsr
⭐
23
[CoRL 2020] Learning 3D Dynamic Scene Representations for Robot Manipulation
Stockast
⭐
23
Predict stock market pricing over 180 minutes using Black-Scholes stocastic modelling and parallel Monte-Carlo simulations.
Modelgrid
⭐
23
A Minimalistic Framework for Creating, Managing and Training Multiple Caret Models
User Profiling In Social Media
⭐
22
Predicting gender, age and personality traits of a user from profile images, status and likes
Automl_comparison
⭐
22
Comparison of automatic machine learning libraries
Recommender System
⭐
21
In this code we implement and compared Collaborative Filtering algorithm, prediction algorithms such as neighborhood methods, matrix factorization-based ( SVD, PMF, SVD++, NMF), and many others.
Kaggle
⭐
21
Kaggle Kernels (Python, R, Jupyter Notebooks)
Pyncov 19
⭐
20
Pyncov-19: Learn and predict the spread of COVID-19
Zs Data Science Challenge
⭐
19
A Data science challenge - "Mekktronix Sales Forecasting" organised by ZS through Hackerearth platform. Rank: 223 out of 4743.
Statistical Learning Using R
⭐
16
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.
Jd Prediction
⭐
16
京东JData算法大赛-高潜用户购买意向预测
Mljar Api R
⭐
15
R wrapper for MLJAR API
Ufc_fight_predictor
⭐
14
UFC bout winner prediction using neural nets.
Ndd
⭐
14
Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity
Datafsm
⭐
13
Machine Learning Finite State Machine Models from Data with Genetic Algorithms
Henosis
⭐
13
A Python framework for deploying recommendation models for form fields.
Adaptive Forex Forecast
⭐
12
An adaptive model for prediction of one day ahead foreign currency exchange rates using machine learning algorithms
Solar Forecasting Rnn
⭐
12
Multi-time-horizon solar forecasting using recurrent neural network
Mlr
⭐
11
Multiple linear regression with statistical inference, residual analysis, direct CSV loading, and other features
Driving Behavior Risk Prediction.
⭐
11
2018平安产险数据建模大赛 驾驶行为预测驾驶风险
Ds_portfolio
⭐
11
Trilearn
⭐
9
Bayesian structure learning and classification in decomposable graphical models
Predicting Bitcoin Price Variations
⭐
8
Predicting Bitcoin Price Variations using Bayesian Regression
Datasci Firerisk
⭐
8
This project attempts to model and acquire data from SF OpenData - and other sources - to predict the relative risk of fire in San Francisco’s buildings and public spaces.
Spkit
⭐
8
Signal Processing Toolkit, including ML models with visualization
Pytolemaic
⭐
8
Toolbox for analysis of model's quality and model's description. For further details see
Timeatlas
⭐
8
A time series data manipulation tool for Python
Real_estate_machine_learning
⭐
7
Modelling real estate market in Budapest using machine learning
Predicting Baseball Statistics
⭐
7
Predicting Baseball Statistics: Classification and Regression Applications in Python Using scikit-learn
Capstoneproject_house_prices_prediction
⭐
7
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.
Ai Hacktoberfest
⭐
7
Welcome to the Hacktoberfest Challenge for Artificial Intelligence / Machine Learning ! Today we will be assessing your skills to Predict Forest Fire Areas given its various parameters!
Cryptocurrency_market_prediction
⭐
6
Time Series based analysis of cryptocurrency prediction using deep learning models
Capstone Yield Prediction And Variety Recommender
⭐
6
Yield Prediction and Variety Recommendations for Southern India
Ssgpr
⭐
6
Sparse Spectrum Gaussian Process Regression
Predict The Damage To A Building Ml Challenge
⭐
6
A Machine Learning challenge #6 - "Predict the damage to a building", organised by Hacker earth. Rank: 242 out of 7540 participants.
Fakenewsjedi
⭐
6
Workspace for the Global AI Hackthon ("Make News Real Again" Challenge): Data Scraping & Cleaning, Text Analytics/NLP, Predictive Modeling, and Feature Engineering in Python
Machine_learning_projects
⭐
6
In this projects is build for some machine learning algorithms. It is used for beginner to intermediate. so i hope that's used for all.
Text Message Classification
⭐
6
Classify messages as Spam or Ham using a simple Naive bayes classifier. After training the Model , next deployment of a web app build on shiny to filter text messages.
Bayesianopttools
⭐
6
Bayesian Optimization and Uncertainty Analyses Tools
Predicting_money_spent_at_resort
⭐
6
It is From Analytics Vidhya Hackathons, Sponsored by Club Mahindra. It is based on Regression Problem, Where Accuracy matters the most, It is measured by RMSE Score. Different Techniques such as Stacking, Ensembling, Boosting and Scientific Operations such box-cox Operations to reduce skewness of the data.
Predicting Paid Amount For Claims Data
⭐
6
Introduction The context is the 2016 public use NH medical claims files obtained from NH CHIS (Comprehensive Health Care Information System). The dataset contains Commercial Insurance claims, and a small fraction of Medicaid and Medicare payments for dually eligible people. The primary purpose of this assignment is to test machine learning (ML) skills in a real case analysis setting. You are expected to clean and process data and then apply various ML techniques like Linear and no linear models like regularized regression, MARS, and Partitioning methods. You are expected to use at least two of R, Python and JMP software. Data details: Medical claims file for 2016 contains ~17 millions rows and ~60 columns of data, containing ~6.5 million individual medical claims. These claims are all commercial claims that were filed by healthcare providers in 2016 in the state of NH. These claims were ~88% for residents of NH and the remaining for out of state visitors who sought care in NH. Each claim consists of one or more line items, each indicating a procedure done during the doctor’s visit. Two columns indicating Billed amount and the Paid amount for the care provided, are of primary interest. The main objective is to predict “Paid amount per procedure” by mapping a plethora of features available in the dataset. It is also an expectation that you would create new features using the existing ones or external data sources. Objectives: Step 1: Take a random sample of 1 million unique claims, such that all line items related to each claim are included in the sample. This will result in a little less than 3 million rows of data. Step 2: Clean up the data, understand the distributions, and create new features if necessary. Step 3: Run predictive models using validation method of your choice. Step 4: Write a descriptive report (less than 10 pages) describing the process and your findings.
Stock Market Analysis
⭐
6
Sklearn2sql Demo
⭐
5
Demo of an In-database processing tool for scikit-learn
Amazon Sagemaker Predictive Maintenance Deployed At Edge
⭐
5
This workshop will familiarize you with some of the key steps towards building an end-to-end predictive maintenance system leveraging Amazon SageMaker, Amazon Polly and the AWS IoT suite.
Air
⭐
5
Real-time data stream classification and knowledge generation engine with no dependencies
Movie Revenue Rating Prediction From Imdb Movie Data
⭐
5
Movie Revenue & Ratings Prediction Using 5000 IMDB Movies [Python, Machine Learning, GitHub]
Modelcreator
⭐
5
Simple python package for creating predictive models
Nfl Predictions
⭐
5
NFL is one of the most followed game having millions of followers all around the world.The current work involves the prediction of NFL match results by using a custom model incorporating a deep neural network and ticket prices for the match using regression modelling. This work considers the most important factor in NFL, the momentum. The prediction model has the capability to get better accuracy than previous models reported. All predictions are made for Week 17 by learning from Week 1 –16’s data.
Python Machine Learning Projects
⭐
5
IMDB Score Prediction
Machine Learning
⭐
5
A comprehensive comparison of decision tree and random forest for cancer classification.
Data Analysis On Hospital Readmission Data
⭐
5
Analysis on hospital readmission data for diabetic patients using R and Tableau
Machine_learning_and_equity_index_returns
⭐
4
Machine Learning and Equity Index Returns
Recorder
⭐
4
lightweight toolkit to validate new observations when computing their corresponding predictions with a predictive model
New York Taxi Demand Prediction
⭐
4
Predicting demand of Taxi in a given area and location of New York City.
Deep Learning Framework For Financial Time Series Prediction In Python Keras
⭐
4
Randomly partitions time series segments into train, development, and test sets; Trains multiple models optimizing parameters for development set, final cross-validation in test set; Calculates model’s annualized return, improvement from buy/hold, percent profitable trades, profit factor, max drawdown
Stanford Mura
⭐
4
My baseline solution for Stanford Mura Bone X-Ray Problem as mentioned in the paper.
Pokerman
⭐
4
Predicting poker hands.
Python For Sharing
⭐
4
Python things I want to share with the world
Github Collaboration Network
⭐
4
Using GitHub's API to find social connections via Pull Requests and Issues.
Agnosticbayesensemble.jl
⭐
4
This Package comprises a collection of ensemble algorithms for prediction, in order to improve the predictive performance for classification and regression problems.
Drifter
⭐
4
Concept Drift and Concept Shift Detection for Predictive Models
Shaving Blades Analysis
⭐
4
An analysis of the shaving blades market in the United States.
Pgdds Iiit Bangalore
⭐
4
A set of projects I worked on as part of my PG Diploma in Data Science Program
Taste
⭐
4
Code accompanying our ACM CHIL paper: "TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records"
Data_science_and_data_driven_decisions
⭐
4
MIT xPRO data science course
Simple Pose Estimation Torch Techi
⭐
4
Only 200 lines code to predict single person pose estimation. A simple prediction module of "Simple Baselines for Human Pose Estimation and Tracking".
K Vol
⭐
4
Market price indicator based on book depth and global volume
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