GitHub topics: lasso-regression-model

Repositories

Marlyn-Mayienga/Machine-Learning-Insights-into-Titanic-and-Wine-Data

This project applies machine learning techniques to two datasets: the Titanic dataset for survival prediction and wine datasets (red and white) for quality analysis. It explores decision trees, KNN, logistic regression, and LASSO regression to uncover patterns, evaluate model performance, and provide actionable insights.

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UFSaidu/Identification_of_TBI_biomarkers_using_Machine_learning

This repository contains code for Machine learning-based identification of diagnostic biomarkers and therapeutic targets for traumatic brain injury.

Language: R - Size: 11 MB - Last synced at: 24 days ago - Pushed at: 24 days ago - Stars: 0 - Forks: 0

MAHMOUD2ABDALLAH/Bike-Sales

It was a competition on KAGGLE for prediction on the most sales products on bikes via their features

Language: Python - Size: 358 KB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 2 - Forks: 0

garfsters/Real-Estate-Price-Prediction

Using OLS regression (and Ridge and Lasso to compare), we worked on a project that uses a dataset to predict housing prices based on user inputs on house details.

Language: Python - Size: 252 KB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

Assem-ElQersh/Kaggle-Housing-Prices-Prediction

House-Prices Advanced Regression Techniques Competition Solution

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saadhaniftaj/logistic--lasso-regression-data-analysis

Iris dataset analysis with logistic and Lasso regression, using coordinate descent for feature selection and binary classification. Includes preprocessing and data visualizations

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shwetajanwekar/Capstone-project_1

Capstone project_1 include python code for best fit regression model, SQL feature store and tableau dashboard

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SandeepGitGuy/Advanced_Regression_model_Housing_Assignment

Advanced Regression model on Housing Data from Australia for my Upgrad - IIITB AI ML PG Course

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Srking501/mas8404_coursework

A summative coursework for MAS8404 Statistical Learning for Data Science

Language: R - Size: 731 KB - Last synced at: about 2 months ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

HakimGhlissi/Car-Price-Prediction-using-Linear-and-Lasso-Regression

Built a car price prediction from Kaggle dataset solution using Linear Regression and also Lasso Regression

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itzKshitijaC/Algerian-Forest-Fire-Prediction

In this project I built machine learning models using Multiple Linear Regression, Ridge regression, Lasso regression, Elasticnet regression and then created a pickle file of the regression model which gave best accuracy

Language: Python - Size: 225 MB - Last synced at: 12 months ago - Pushed at: 12 months ago - Stars: 0 - Forks: 0

ucheynna/ML-cancer-remission

Developed regularization and tree-based machine learning models to predict remission status in a cohort of 5059 patients. Elastic net and Random Forest models were compared on F1 scores accuracy, sensitivity, specificity, and AUC ROC.

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Seniorveiga/Machine-Learning-Basics

Approach to some basic Machine Learning Techniques. It is necessary to concatenate/specify the route of the .csv file that is associated to the Script to make it work.

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eeshwarib23/Airbnb-regression-analysis

Size: 4.27 MB - Last synced at: 9 months ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 0

rociobenitez/AirbnbPricingModel

A Machine Learning exploration evaluating various models to predict Airbnb prices, culminating in an optimized Gradient Boosting Regressor.

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ketanmakde/Advanced_regression_assignment

Building Advanced regression models (Lasso and Ridge) for house price prediction in the Australian market

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dorisyan1122/Crime-Predictive-Modeling

Final Group Project for Advanced Data Science for Public Policy @ McCourt

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sagprr/Machine_Learning

A repository containing machine learning projects and models.

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akashkriplani/Advanced-Regression-Surprise-Housing

Predictive Analytics for Real Estate Investment: A Regression Model Approach for Surprise Housing in the Australian Market using Regularization methods (Ridge and Lasso)

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amoustakis/Statistical-Modeling-data-analysis

A series of Statistical Modelling assignments with the use of R. Applications of Linear, Polynomial, Logistic and Poisson Regression in various datasets

Language: R - Size: 2.2 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 0

AjNavneet/RegressionModels_Numpy_LassoRidgeDT

Regression models(lasso, ridge, DT) using NumPy.

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DataSpieler12345/r-for-data-science

"Learning R for data scientists." This phrase describes the process of acquiring the skills and knowledge necessary to use the R programming language for data analysis.

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AlrikF/Data-Science-Projects

A collection of numeorus tasks such as classification, regression, time series forecasting etc. on a number of datasets using different algorithms such as random forest, SVM, Naive Bayes, Ensemble, perceptron etc in addition to data cleaning and preparation.

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markkukuismin/BONE

Genetic assignment of individuals to known source populations using network estimation tools.

Size: 7.55 MB - Last synced at: over 1 year ago - Pushed at: over 2 years ago - Stars: 1 - Forks: 0

sfansaria/Machine-Learning

ols_regression, Simple_Linear_Regression,univariate_Polynomial_Regression,Bayesian_Regression

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MYoussef885/Car_Price_Prediction

The "Car Price Prediction" project focuses on predicting the prices of cars using machine learning techniques. By leveraging popular Python libraries such as NumPy, Pandas, Scikit-learn (sklearn), Matplotlib, Seaborn, Lasso regression, and Linear regression, this project provides a comprehensive solution for accurate price estimation.

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Ujju-27/House-Prediction

House Price Prediction can help the customer to arrange the right time to Purchase a House. It is An - ML based Approach which Predicts the Estimated Price of Housing in Mumbai City.

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Masihsoniya/House-price-prediction

Predicting house price

Language: Jupyter Notebook - Size: 211 KB - Last synced at: 18 days ago - Pushed at: over 2 years ago - Stars: 2 - Forks: 0

somanathkshirsagar/-LeastSquare

LeastSquare is a web application developed with the objective of predicting the price of used cars. The project follows the life cycle of a data science project and incorporates various tools and techniques such as machine learning, regression analysis, linear regression, polynomial regression, Lasso regression, Ridge regression, and Streamlit.

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awiksshiith-narang/Lasso_Regression

This model trains according to the data and makes a Polynomial Regression curve of degree 16. The model is regularized using Lasso regression. It also compares the predicted values with original outputs and for different alphas.

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AlaaMahmoud95/Concrete-Compressive-Strength

Regression Machine Learning Project

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LokeshSreenathJ/House-Price-Prediction

Built a Gradient Boosting model by employing Lasso Regularization and Hyper-parameter tuning

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Krystkowiakk/IMDB-TV-Rating-Predictor

Metis project 2/7

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adiag321/Boston-AMES-Housing-Price-Prediction

In this project, we will predict the price for AMES House and learn Machine Learning Algorithms, different data preprocessing techniques such as Exploratory Data Analysis, Feature Engineering, Feature Selection, Feature Scaling and finally to build a machine learning model.

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Related Keywords

lasso-regression-model 45 linear-regression 17 ridge-regression-model 15 machine-learning 15 ridge-regression 12 data-science 10 regression-models 8 logistic-regression 7 python 7 machine-learning-algorithms 6 lasso-regression 6 r 5 exploratory-data-analysis 5 regression 5 elasticnet-regression 5 data-visualization 4 random-forest 4 seaborn 3 feature-engineering 3 numpy 3 prediction-model 3 pandas 3 predictive-modeling 3 xgboost 3 data-analysis 3 randomforestregressor 3 polynomial-regression 3 lasso 3 supervised-learning 3 sklearn 3 eda 3 random-forest-regression 3 python3 3 skewness 2 scikit-learn 2 data-preprocessing 2 ml 2 machinelearning 2 lassoregression 2 correlation-analysis 2 elastic-net 2 regularization 2 hyperparameter-tuning 2 rstudio 2 matplotlib 2 decision-tree-regression 2 principal-component-analysis 2 decision-trees 2 lgbmregressor 2 kaggle-competition 2 gradient-boosting-regressor 2 hyperparameter-optimization 2 scikitlearn-machine-learning 2 matplotlib-pyplot 2 ols-regression-model 2 support-vector-regression 2 regression-analysis 2 smartphone-sensor-data 1 seaborn-plots 1 flask-application 1 machie-learning 1 accuracy-score 1 bayesian 1 mean-square-error 1 kurtosis 1 mean-absolute-error 1 log 1 normal-distribution 1 gradient-boosting 1 boosting 1 probability-of-churn 1 pruning-optimization 1 time-series-classification 1 assignment-analysis 1 r-package 1 most-explicative-variable 1 undirected-graph 1 bayesian-regression 1 simplebayesianlinearregressionusingtensorflow 1 simplelinearregression 1 univariatepolynomialregression 1 knn-reg 1 correlation-coefficient 1 google-colab-notebook 1 churn-prediction 1 numpy-arrays 1 pandas-dataframe 1 beutifulsoup 1 sgd 1 panel 1 extra-trees 1 imdb-rating 1 imdb-tv-shows 1 deep-learning 1 imdb-webscrapping 1 feature-scaling 1 scraping 1 statsmodels 1 crossvalidation 1 hyper 1