sklearn-estimator
There are 1 repository under sklearn-estimator topic.
- Language:Python 10
sid321axn / Udacity-MLND-Capstone-Gold-Price-Prediction
Capstone Project Gold Price Prediction using Machine learning Approach for Udacity Machine Learning engineer Nanodegree Program
Language:Jupyter Notebook 8lewis-morris / Skperopt
A hyperopt wrapper - simplifying hyperparameter tuning with Scikit-learn style estimators.
Language:Python 5xhan97 / inne
A scikit-learn-compatible module for Isolation-based anomaly detection using nearest-neighbor ensembles
Language:Python 4sousablde / Plagiarism-Detection-AWS-Sagemaker
This project is part of Udacity machine learning nanodegree, using an sklearn estimator for plagiarism detection
Language:Jupyter Notebook 3sainipankaj15 / PAIR-TRADING-STRATEGY-USING-RANDOM-FOREST
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.
Language:Jupyter Notebook 2mynkpl1998 / atppredict
Improvement and Implementation of Paper 'Identification of ATP binding residues of a protein from its primary sequence'
Language:Python 1RazHoshia / Wids2021EvalMLRapids
combination of EvalML with Rapids for the WiDS 2021 competition
Language:Python 1- Language:Python 0
Logambal05 / Industrial-Copper-Modelling
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.
Language:Jupyter Notebook 0swagatika15 / FACE-CLASSIFICATION
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 glasses). All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position (with tolerance for some side movement). The “target” for this database is an integer from 0 to 39 indicating the identity of the person pictured. Each of the sample images needs to be classified in the classes ranging from 0 to 39. PCA has been applied to reduce the dimensionality. Then various classification and regression techniques are used with and without using PCA and the accuracy and time taken by the algorithms are recorded. Algorithms used: SVM, KNN, logistic regression, neural networks, linear regression and random forests.
Language:Jupyter Notebook 0- Language:Python 0
vale-salvatelli / myML
A repo that contains helpers for machine learning modeling
Language:Python 0- Language:Python
- Language:Python
