gene classification based on partial-order rule mining

Problem Moving from traditional energy plans powered by fossils fuels to unlimited renewable energy subscriptions allows for instant access to clean energy without heavy investment in infrastructure like solar panels, for example. One clean energy source that has been gaining popularity around the world is wind turbines. Turbines are massive str…

A comprehensive case study on implementing predictive modeling in insurance

Zepto Order Analysis to Predict Unique Orders from a range of Products

LASSO and Boosting for Regression on Communities and Crime data

Datascience hands on code

Predicted the breast cancer in patient using Ensemble Techniques and evaluated the model

Use of Weights & Biases to systematically tune and evaluate the hyperparameters of a Gradient Boosting Classifier. The dataset we are working with is the Wine dataset.

KeepCoding Bootcamp Big Data & Machine Learning - Práctica Machine Learning 101

Job Change of Data Scientists Prediction

In this repository, I will share the materials related to machine learning algorithms, as I enrich my knowledge in this field.

CSE601 Course Projects - Fall 2017

MLJ.jl interface for JLBoost.jl

Building classification models to predict if a loan application is approved. Using under-sampling, bagging and boosting to tackle the problem of with unbalanced dataset

Implemented support vector machines, boosting, and decision trees for classification problems. Used cross-validation for improving model accuracy. Plotted different types of learning curves like error rates vs train data size, error rates vs clock time. Compared performance using learning curves and confusion matrices across algorithms.

This project focuses on predicting the IPL scores using Machine learning models with the use of Python using Scikit Learn Library. The model predicts the score after a minimum of 5 overs. The score on Testing data was 94.17%.

classfication of cloud image pixels

Built Random Forest and GBDT using XGBOOST model on Amazon fine food review dataset

Microsoft Bingのランキングの重みを自然言語的に解釈、表現します

In this project we are tryinbg to create unredactor. Unredactor will take a redacted document and the redacted flag as input, inreturn it will give the most likely candidates to fill in redacted location. In this project we are only considered about unredacting names only. The data that we are considering is imdb data set with many review files.…