mean-absolute-error

XinshaoAmosWang/Improving-Mean-Absolute-Error-against-CCE

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

AloRay/GAS-PRICE-FORECASTING

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

rjnp2/deep_learning_from_scratch

SCIFER99/Building-Machine-Learning-Models-with-Python

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

BradyFisher/Housing-Prices-Machine-Learning-Project

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

BradyFisher/Machine-Learning-Titanic-Project

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.

guilhermedom/perceptron-regression-ice-cream-per-temp

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

gurol/BenchMetricsProb

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

jong26/rainfall_prediction_ml

This project used various machine learning algorithms to predict rainfall.

MrRaghav/airbnb-in-berlin-2020

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

mustaffa-hussain/Performance-Metric

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

nikhilt1998/DrivenData-DengAI-Predicting-Disease-Spread

DengAI: Disease spread prediction(DrivenData Challenge)

Thakursiddhesh/Regression-Model-to-Predict-Cement-Compressive-Strength

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.

Ashwin0229/Collaborative-Filtering-K-nearest-neighbors-and-SVM

Using Collaborative Filtering predicting Movie Rating and K-nearest Neighbours & SVM algorithms for Number ClassificationNumber Classification

camythaocta/Regularized-Regression_Predicting-Housing-Price

In linear regression, regularization is a process of making the model more regular or simpler by shrinking the model coefficient to be closer to zero or absolute, ultimately to address over fitting.

GunturWibawa/BetaBankChurnAnalysis

Beta Bank is losing customers monthly. Employees want to focus on client retention. As a Data Scientist, I created a model to predict the chance of a customer leaving, based on past behavior and contract terminations.

LucasMLago/COVID-19-Healthy-Diet

A study about Regression algorithms

newtonsgrandson/under-over-fitting-gui-v1

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

ns-nexus/Rain-Prediction-in-Australia

We are going to use the different classification algorithms to create a model to predict rain in Australia. This project was done as a part of the Honors portion of the IBM Machine Learning Course on Coursera.

Samuel-the-crack/Boston-House-Price-Prediction

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

aaaastark/Intrusion-Detection-System-MQTT-Enabled-IoT

Intrusion Detection System for MQTT Enabled IoT.

EmamulHossen/LinearRegression

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.

guilhermedom/deep-regression-bicycle-rental

TensorFlow deep regression model predicting bicycle rental.

GunturWibawa/SpotifyPopularityProbe

In the digital music era, understanding artist popularity on Spotify is vital. This project taps into Spotify's data, analyzing key factors driving artist prominence. Through our insights, we illuminate what sets successful artists apart in this dynamic platform.

iamkirankumaryadav/Regression

Regression - Bulldozer Sales Price - Kaggle Competition

Lefteris-Souflas/Modern-Slavery-Analysis

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.

LorenzoRottigni/ML-diabets

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

MylieMudaliyar/Feature-Engineering-Techiques-on-House-Values

The feature engineering techniques discussed are - dimensionality reduction(pca), scaling(standard scaler, normalizer, minmaxscaler), categorical encoding(one hot/dummy), binning, clustering, feature selection. These are techniques performed on a dataset consisting of Californian House Prices.

niyazed/forecasting

Templates of statistical and DL forecasting on some synthetic data and lastly predicting sunspot using kaggle sunspot dataset.

nnamanx/olympic-games

I use data from historical Olympic games and try to predict how many medals a country will win based on historical and current data

victorchendra02/Kecepatan-Rata-rata-di-41-koridor-jalan-utama-pada-jam-sibuk-Tahun-2019

Data transformation using linear regression and cross validation (MAE)

Vivek-Tate/Population-Forecast-Prediction

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

Zauverer/excer_regularization