PyTorch-Based Evaluation Tool for Co-Saliency Detection

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

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

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

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.

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.

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

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

DengAI: Disease spread prediction(DrivenData Challenge)

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

This is a project where I use the Random Forest Regression and XGBoost Machine Learning Techniques to held predict the Sales Price of Houses..

Perceptron regressing revenue for an ice cream stand according to temperature.

This project used various machine learning algorithms to predict rainfall.

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.

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)

A machine learning project that predicts car prices based on a dataset.

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

TensorFlow deep regression model predicting bicycle rental.