PyTorch-Based Evaluation Tool for Co-Saliency Detection

Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude’s Variance Matters

This Repository contains scratch implementations of the famous metrics used to evaluate machine learning models.

DengAI: Disease spread prediction(DrivenData Challenge)

This is a project where use the Random Forest Classifier and XGBoost Machine Learning Techniques to held predict what passengers survived the sinking of the Titanic.

Linear regression is also a type of machine-learning algorithm more specifically a supervised machine-learning algorithm that learns from the labeled datasets and maps the data points to the most optimized linear functions.

This repository utilizes time series analysis to predict natural gas prices, aiding informed decisions in the energy market. Through meticulous data preprocessing, visualization, and ARIMA modeling, it provides accurate forecasts. With regression and interpolation techniques, it offers deeper insights for stakeholders, enabling proactive strategies

Jupyter notebook using machine learning techniques to explore the complex drivers of modern slavery. Models from a research paper are replicated and evaluated . Actions also include filling missing data, training regression models, and analyzing feature importance.

Data transformation using linear regression and cross validation (MAE)

Different modeling techniques like multiple linear regression and random forest, etc. will be used for predicting the cement compressive strength. A comparative analysis will be performed to identify the best model for our prediction in terms of accuracy.

Comparing Ridge and LASSO model to find the best accuracy for Home Price Prediction

Build Linear Regression and Mean Absolute Error Models with Python for Machine Learning

TensorFlow deep regression model predicting bicycle rental.

BenchMetrics Prob: Benchmarking of probabilistic error performance evaluation instruments for binary-classification problems

Machine Learning course of Piero Savastano 1: LinearRegression, mean_absolute_error, train_test_split

Population Prediction forecasts the Haggis population on a mountain. Ecologists have recorded the population over five years and have satellite estimates. The goal is to predict the true population 12 months ahead using machine learning and time series analysis techniques. This project is for the COM6509 - Machine Learning and Adaptive Intelligence

This project used various machine learning algorithms to predict rainfall.

A data mining project to analyse Airbnb's data of Berlin for the year 2020 using KDD

to observing mean absolute error with decision tree regression for train and test