There are 1 repository under joblib topic.
Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
Framework for correlating two or more well logs using feature vectors generated from CNN's in Pytorch
π 30 Days of Data Science is a daily challenge to guide you through Data Science essentials. From basics to advanced, this repo offers clear examples, practical exercises, and resources to help you master Data Science, one day at a time. Whether you're new or refining your skills, this challenge has something for you. Join the journey now! π
Joblib-like interface for parallel GPU computations (e.g. data preprocessing)
The aim of this project is to develop a solution using Data science and machine learning to predict the compressive strength of a concrete with respect to the its age and the quantity of ingredients used.
A machine learning exercise using the Spotify "hit predictor" dataset, with data analysis of past "hits" by decade. Deployment using Flask via Heroku.
Spam SMS Detection Project implemented using NLP & Transformers. DistilBERT - a hugging face Transformer model for text classification is used to fine-tune to best suit data to achieve the best results. Multinomial Naive Bayes achieved an F1 score of 0.94, the model was deployed on the Flask server. Application deployed in Google Cloud Platform
A step-by-step guide to master various aspects of Joblib for parallel computing in Python
π A flexible Python library for easy handling and conversion of Hierarchical, Tabular, and Serialized data formats.
A GitHub WebCrawler
A machine learning project to predict smoking status (Smoker/Non-Smoker) using health and lifestyle data. It includes data preprocessing, model training, evaluation, visualizations, and FastAPI-based deployment, supporting CI/CD and multiple datasets for robustness.
PyPOLAR is a Python-based app for analyzing polarization-resolved microscopy data to measure molecular orientation and order in biological samples
A Proximal Policy Optimization Approach to Detect Spoofing in Algorithmic Trading
predict the winning horse with supervised machine learning models (lucky to have 100% accuracy on small test data)
A machine learning pipeline for classifying cybersecurity incidents as True Positive(TP), Benign Positive(BP), or False Positive(FP) using the Microsoft GUIDE dataset. Features advanced preprocessing, XGBoost optimization, SMOTE, SHAP analysis, and deployment-ready models. Tools: Python, scikit-learn, XGBoost, LightGBM, SHAP and imbalanced-learn
A Regression Model that predicts a fish's weight based on its specie, length, width & height.
An IA model that detects whether a given verse is from the Bible or not
A basic malware detector using Machine Learning
Predicts which telecom customers are likely to churn with 95% accuracy using real-world data features from usage, billing, and support data. Implements Sturges-based binning, one-hot encoding, stratified 80/20 train-test split, and a two-level ensemble pipeline with soft voting. Achieves 94.60% accuracy, 0.8968 AUC, 0.8675 precision, 0.7423 recall.
This project aims to detect and classify fake news using Natural Language Processing (NLP) and Machine Learning techniques. The model is trained to identify whether a news article is real or fake based on its content.
:jack_o_lantern:Kaggle-Comptetion-Titanic-Dataset(Codeperfectplus):trophy:
Python scripts to download, process, and analyze the New York City Taxi and Limousine Commission (TLC) Trip Record Data dataset
Machine Learning Project for recommendations of music genre based on age and gender
RandomForest Regressor Model ML for predicting Price of House.
An efficient text classification pipeline for email subjects, leveraging NLP techniques and Multinomial Naive Bayes. Easily preprocess data, train the model, and categorize new email subjects. Ideal for NLP enthusiasts and those building practical email categorization systems using Python.
Python utility for fetching any historical data using caching. Suitable for news, posts, weather, etc.
Reinforcement Learning: Q-Learning and Deep Q-Learning to train artificial agents that can play the famous game of Nim.
Anti Spam Filter, a spam filter ποΈ which uses a model made out of MultinomialNB algorithm π from scikit-learn π to classify spam and complaints.
Primary aim of this project is to build machine learning model that give the should able to predict the sales of the different stores of Big Mart according to the provided dataset.
Python scripts that scrape US gas prices
StockSage is a production-ready RESTful API built with FastAPI and Docker to serve machine learning models. It allows users to send stock data and receive price predictions using the Prophet model. Designed for scalability, it follows best practices in API deployment, containerization, and production model serving.
This project aims to predict credit risk for individuals applying for loans, classifying whether they will default based on features such as age, income, employment length, loan amount, interest rate, percentage of income, credit length, home ownership, and loan intent.
Credit Score Prediction is a machine learning project that classifies credit scores ('Good', 'Standard', 'Poor') using a streamlined pipeline. It involves data extraction, cleaning, and preprocessing, with key techniques like Mutual Information for feature selection, PCA for dimensionality reduction, and XGBoost for efficient and accurate model tr
An AI tool that predicts potential exoplanets using NASA's Kepler data. Users can enter a celestial object's transit data into a web app and get an instant prediction, helping to distinguish between potential exoplanets and false positives.