Polynomial regression and classification with sklearn and tensorflow

• UpdatedNov 9, 2023
• Python

Capstone Project Gold Price Prediction using Machine learning Approach for Udacity Machine Learning engineer Nanodegree Program

• UpdatedFeb 6, 2019
• Jupyter Notebook

A scikit-learn-compatible module for Isolation-based anomaly detection using nearest-neighbor ensembles

• UpdatedAug 30, 2023
• Python

A hyperopt wrapper - simplifying hyperparameter tuning with Scikit-learn style estimators.

• UpdatedApr 4, 2020
• Python

This project is part of Udacity machine learning nanodegree, using an sklearn estimator for plagiarism detection

• UpdatedNov 19, 2019
• Jupyter Notebook

Developed a model using Random Forest algorithm to get prediction of user defined number of stocks to go long & short in all trading sessions of upcoming year. Achieved 14.04% CAGR with 100% profitability in all seven years of backtested data. The model outperformed the index in 5 years out of the total 7 years of testing.

• UpdatedOct 20, 2022
• Jupyter Notebook

Improvement and Implementation of Paper 'Identification of ATP binding residues of a protein from its primary sequence'

• UpdatedDec 16, 2017
• Python

combination of EvalML with Rapids for the WiDS 2021 competition

• UpdatedApr 2, 2021
• Python

library for basic data science tasks.

• UpdatedSep 20, 2019
• Python

• UpdatedApr 13, 2018
• Python

The aim of this project is to classify the faces. Olivetti Faces dataset has been used. In this dataset there are ten different images of each of 40 distinct subjects. For some subjects, the images were taken at different times, varying the lighting, facial expressions (open / closed eyes, smiling / not smiling) and facial details (glasses / no …

• UpdatedNov 26, 2019
• Jupyter Notebook

A repo that contains helpers for machine learning modeling

• UpdatedMay 12, 2020
• Python

🔴 dred = dimension reducing for machine learning (suit to sklearn)

• UpdatedNov 15, 2019
• Python

scikit-learn compatible estimators for various kNN imputation methods

• UpdatedFeb 21, 2025
• Python

Sales and pricing data that is subject to noise and skewness are managed with difficulty thanks to the Copper Industry Sales and Leads Prediction Project. In the industry, manual forecasts can be inaccurate and time-consuming. The creation of machine learning models is the main goal of this project in order to overcome these obstacles.

• UpdatedDec 17, 2023
• Jupyter Notebook