mean-absolute-error
There are 40 repositories under mean-absolute-error topic.
zzhanghub/eval-co-sod
PyTorch-Based Evaluation Tool for Co-Saliency Detection
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
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
CamilaJaviera91/Prediction-of-Housing-Prices-Using-Linear-Regression
This project provides tools to search for datasets on Kaggle, download and preprocess them, and perform predictions using a Linear Regression model. It includes interactive text-based user interfaces built with `curses`.
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
maheera421/Bulldozer-Price-Prediction-Model
Prediction of the auction prices of bulldozers using historical data.
maheera421/Car-Price-Prediction-Model
A machine learning project that predicts car prices based on a dataset.
manjugovindarajan/ReCell-Supervised-Learning-
Analyze used devices dataset, build a model to develop a dynamic pricing strategy for used/refurbished devices, identify factors that significantly influence price.
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
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
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.
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
maheera421/California-Housing-Price-Prediction-Model
A regression model to predict housing prices based on various features.
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.
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)
Wb-az/MLib-PySpark-SoundLevel-Prediction
Creates a ML Pipeline leveraging PySpark SQL and PySpark MLib to predict sound level