Contains our Approach for the competition organized at Udyam'21
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Updated
Apr 20, 2021 - Jupyter Notebook
Contains our Approach for the competition organized at Udyam'21
Example notebooks to produce the models used in the SexEst web application.
A machine learning based forecasting system for taxi demand prediction
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
This project is about to detecting the text generated by different LLM given prompt. The instance is labeled by Human and Machine, and this project utilised both traditional machine learning method and deep learning method to classify the instance.
This repo is a hands-on implementation of End-to-End MLOps Pipeline, with LGB hyperparameter tuning, MLflow model tracking and CI/CD with GitHub Actions.
Implémentation d'un modèle de scoring (OpenClassrooms | Data Scientist | Projet 7)
This approach has the potential to create accurate, generalizable and adaptable machine learning methods that effectively and sustainably address agricultural tasks such as yield prediction and early disease identification.
Spectral type classification using LGBM and deployed using FastAPI, Pydantic, and Docker
Music Genre Recommender website that can identify and recommend 10 different genres of music using Light Gradient Boosting Machine (LGBM). An accuracy of 90% was achieved on the test set by tuning the hyperparameters of the model with Optuna.
End to end Heart Diseases Prediction Model with webapp using Flask
Machine Learning model for heart failure prediction using LGBM Classifier.
Using LGBMClassifier to solve To-Be Challenge, which is a machine learning challenge on CodaLab Platform that aims to adress the problems of medical imbalanced data classification.
Learning to Rank - Cross Sell
Early prediction of Mortality Risk among Covid -19 Patients in early stages when patients gets admitted into the hospital.
Predicting transaction fraud using classification problems such as Guardian Boosting as well as user interfaces using Streamlite
This repository contain my final projekt on the Data science Skillbox school on the topic: "Development of a machine learning algorithm to predict the behavior of customers of the "SberAvtopodpiska"
It's the Repo having a .ipynb file with bank customer churn prediction using various classifiers and machine learning models
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