sudhanshusinghaiml/End-to-End-Real-Estate-Price-Prediction-Model

This project for price prediction of Real Estate Property. Model is trained on dataset with Property Price as the Target Variable. The model takes into account the important factors

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invictusaman/Insurance-Cost-Analysis-Regression

Showcasing Simple Linear, Multiple Linear, Polynomial and Ridge Regression on Insurance Cost Dataset to predict insurance price. Also, I have generated a report using Quarto.

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krishcy25/Building-several-Regression-MachineLearning-Algorithms-for-ContinuousOutcomePrediction

This repository focuses on building several Regression Models-Linear Regression, XGBoost Regressor, Ridge Regression, Lasso Regression, Polynomial Regression that predicts the continuous outcome (House Prices) along with several Data Preparation Techniques (Transformations/Scaling, Imputation, Filtering of Outliers, Handling of correlated features, One Hot Encoding)

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Jenil311/Application-of-Covid-19-Spread-Analysis

The objective of this project is to study the COVID-19 outbreak using basic statistical techniques and make short term predictions using ML regression methods.

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Jybbs/Derailleur

A run-through of various data science techniques to analyze and visualize bike sharing trends, using the UCI Bike Sharing Dataset.

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MahmoudNamNam/NYC_Taxi_Trip_Duration_Predection

The project aims to predict NYC taxi trip durations using advanced regression techniques. We utilized Polynomial Linear Regression, Ridge Regression, and Lasso Regression for feature extraction and achieved a validation R² score of 0.67. Feature engineering included KMeans clustering, Haversine distance calculation, and date-time feature extraction

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ahaeusser/compstat_2024

Slides for the 26th International Conference on Computational Statistics (COMPSTAT 2024)

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vn33/Linear-Regression-Polynomial-Regression-Regularization-Assumptions

In this project, we implement a linear regression model and its extensions on a student grades dataset to enhance performance. The workflow includes advanced EDA, data preprocessing, and assumption checks. Key steps: dataset overview, univariate and bivariate analysis, data preprocessing, model building(2nd degree,l1,l2,EN) and result visualization

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FarzadAziziZade/Machine-Learning-Test

Here I upload my ML test scripts written in MATLAB or Python

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hainn2803/Training-Session-Machine-Learning-lab-2023

Training phases in Machine Learning lab at HUST, Spring 2023

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ozerzeynep/KONTR_FIRMA_ANALIZ

Bu proje, Kontr firmasının borsa verilerini kullanarak hisse senedi fiyatlarının gelecekteki değerlerini tahmin etmeye yönelik gelişmiş makine öğrenimi modelleri içerir. Farklı algoritmalarla performans analizi yaparak yatırım kararlarını destekleyici öngörüler sağlar.

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TyrelM10/Predicting-Cancer-Antigen-Levels

Prediction Using Linear Regression Models of Least Squares, Ridge Regression, and Lasso Regression

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ShahzaibWaseem/Python

Python Projects

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itsatefe/Machine-Learning

Machine Learning Algorithms

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nataliabeltranarg/Barcelona_Apartment_Price_Prediction Fork of monbiote/Barcelona_Apartment_Price_Predictor

Forecasting future apartment prices in Barcelona using Linear, Lasso, and Ridge via Python

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ahmedshahriar/Housing-Price-Prediction

Data science project on Housing Prices Dataset regression analysis

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Dev566/EECS-738-and-800

Manual implementation of different models

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Gabya06/graduate_school_admission

US graduate school's admission related data - based on Kaggle

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3zhang/Python-Lasso-ElasticNet-Ridge-Regression-with-Customized-Penalties

An extension of sklearn's Lasso/ElasticNet/Ridge model to allow users to customize the penalties of different covariates. Works similar to penalty.factor parameter in R's glmnet.

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Nizarassad/Pan-Cancer-Analysis

The Cancer Genome Atlas Pan-Cancer analysis project

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kevjh18/PYRO

Ridge Regression Work

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poolakit/Adv_Reg_assignment

This repository has been published for the Advanced Regression assignment to predict house prices in the Australia market

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frank01101/gradient_descent

Gradient descent algorithm from scratch for linear and logistic regression with feature scaling and regularization.

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hirenkumawat/M3_Challenge_MTF

Math Modeling M3 Challenge 🏆 Forecasting Small Farmer Losses using Time Series Analysis (Prophet), Ridge Regression

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IsmaelMousa/house-price-prediction

End to end machine learning pipeline for house price prediction, including exploratory data analysis, data preprocessing, models training & evaluating

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G-akki26/COVID-19

This repository is B.Tech. major project on COVID-19 Global and India Forecast

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radismili/Medical-insurance-costs

EDA, Hypothesis testing and regression models

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imildositoe/python_snippets

Python regression and probabilities snippets

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tboudart/Life-Expectancy-Regression-Analysis-and-Classification

I contributed to a group project using the Life Expectancy (WHO) dataset from Kaggle where I performed regression analysis to predict life expectancy and classification to classify countries as developed or developing. The project was completed in Python using the pandas, Matplotlib, NumPy, seaborn, scikit-learn, and statsmodels libraries. The regression models were fitted on the entire dataset, along with subsets for developed and developing countries. I tested ordinary least squares, lasso, ridge, and random forest regression models. Random forest regression performed the best on all three datasets and did not overfit the training set. The testing set R2 was .96 for the entire dataset and developing country subset. The developed country subset achieved an R2 of .8. I tested seven different classification algorithms to classify a country as developing or developed. The models obtained testing set balanced accuracies ranging from 86% - 99%. From best to worst, the models included gradient boosting, random forest, Adaptive Boosting (AdaBoost), decision tree, k-nearest neighbors, support-vector machines, and naive Bayes. I tuned all the models' hyperparameters. None of the models overfitted the training set.

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HirudikaAnupama/Student-Score-Prediction-Linear-Regression

Here the prediction and analysis of student scores using selected features is done entirely by linear regression machine learning algorithm. This project covers all methods of linear regression theory.

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Azie88/Regression-Energy-Data

Machine Learning Regression Model to Predict Energy Efficiency of Buildings

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micahwiesner67/NY_100YR_Flood_Prediction

I created multiple models to predict the discharge volume of a 100 year flood on rivers in NY state. The discharge of 100 year flood events is dependent upon watershed drainage area, and elevation among other variables.

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c0ra/RRSARMMI

This repository contains the code and data necessary to reproduce the results presented in the paper "Ridge Regularization for Spatial Auto-regressive Models with Multicollinearity Issues" submitted to Advances in Statistical Analysis (AStA).

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Saipavan790/Regression-Analysis

Prediction of Insurance Charges using Regression

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tweichle/Predicting-Baseball-Statistics

Predicting Baseball Statistics: Classification and Regression Applications in Python Using scikit-learn and TensorFlow-Keras

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Niteshchawla/Jamboree-LinearRegression

Analysis will help Jamboree in understanding what factors are important in graduate admissions and how these factors are interrelated among themselves. It will also help predict one's chances of admission given the rest of the variables.

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rakeshjasti/Car-MPG

Predicting miles per gallon (MPG) for a car using UCI dataset

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devavinothm/house-price-predictor

House Price Prediction using Ridge Linear Regression Model and Bangalore Dataset

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Rjonah321/House-price-estimator

A Ridge regression model used to estimate house prices.

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Hadley-Dixon/DiabetesRegression

A MLR algorithm that analyzes diabetes data in African Americans to find factors predicting diagnosis

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VaishnaviThakre/Credit-Card-Fraud-Detection-Regression

This repository contains predictive ml model

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gaurav-bhadane/Fire_Weather_Index_Predictor

Fire Weather Predictor made using Ridge Regression on Algerian Forest Fire Dataset to forecast fire occurrences based on meteorological parameters. Utilizes Python with Flask for web interface. Predictions delivered via an interactive dashboard.

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FaresAlbadrawi/alx-regression

Regression exercises and projects done at alx training

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RohithAthalury/Salary-Prediction-Model-for-New-Hires

This model utilizes regression models and accurately predicts employee salaries based on experience, previous CTC, and job roles, promoting fair salary structures and optimizing resource allocation for streamlined HR operations.

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mhadeli/Appliances-Energy-Prediction

Predict the energy consumed by appliances using custom-coded Machine Learning models and Algorithms like PCA, Neural Networks, Lasso, Ridge, and Linear 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

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Davityak03/Ridge-and-Lasso-Regression

Implented ridge and lasso regression by understanding the use of parameters

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dteuscher1/Adjusted-Plus-Minus

Adjusted Plus Minus Models for WNBA players from the 2019 season. Adjusted Plus Minus (APM) and Regularized Adjusted Plus Minus (RAPM) models were fit providing an all in one player value metric for the WNBA.

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shubhanshu1995/Advanced-Regression-using-Ridge-and-Lasso

We are required to build a regression model using regularization in order to predict the actual value of the prospective properties and decide whether to invest in them or not.

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sadovsd/crypto-price-prediction-ML-pipeline

Forecasting Ethereum return quantiles using a handful of different statistical learning models and selecting the best based on out of sample error. Hopsworks feature store and model registry is used to automate the process. Ethereum quantile returns are predicted daily and displayed on a Streamlit dashboard.

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aditya-xq/House-Price-Predictor-Using-Machine-Learning 📦

Language: Python - Size: 6.84 KB - Last synced at: about 2 months ago - Pushed at: over 6 years ago - Stars: 0 - Forks: 1

AnchaNarasimha/Salary-Prediction-Model-for-New-Hires

This model utilizes regression models and accurately predicts employee salaries based on experience, previous CTC, and job roles, promoting fair salary structures and optimizing resource allocation for streamlined HR operations.

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siddikayyappa/PCA-Regression-Nearest-Neighbours

This is the source code of the work for Assignment-2, Statistical Methods in AI, 5th Semester, IIITH, '22

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tsitsimis/constrainedlr

Drop-in replacement of sklearn's Linear Regression with coefficients constraints

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AndreisSirlene/House-Price-Prediction

Prediction of the sales price of houses in King County, Washington State – U.S.

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tuhinmallick/Stochastic-methods-Project

Multivariate time series forecasting(MLTS) has been a mainstream tool for forecasting in economics, traffic modelling, economics, future shipments, temperature forecasts(temperature forecast solely on previous year data(as shown in "Lugano temperature forecast", it requires weather modelling too). The basic assumption in multivariate time series forecasting is that different variables are dependent on each other, but existing methods are not very efficient at finding a good neighbourhood relationship. It can be said that they fail at fully exploiting the spatial dependencies between the multiple features of a time series. Change in a single variable directly or indirectly leads to change in the other variables, for eg. change in temperature would directly effect the electricity consumption of a given area It can indirectly affect the stock price of a certain company. Using MLTS we can capture the trends and overall changes seen with dynamically changes variables. In this work we conduct a study between Graph Neural networks with (Ridge and Lasso) Regression to understand the kind of results we obtain from Machine learning models and classical statistical method. In the present methods based on statistics, such as Gaussian process models and auto regressive models assume linear dependency among variables.

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Ashutosh27ind/ridgeLassoCarPricePrediction

Car Price Prediction using Ridge and Lasso

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Ashutosh27ind/advanceRegressionHousePricePrediction

This is for Kaggle competition https://www.kaggle.com/c/house-prices-advanced-regression-techniques/ .

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Ashutosh27ind/creditAmountPredictionHackathon

Skillenza Upgrad DataScience Hackathon : Rank #18 in Leaderboard

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hanafibenaissa/car-price-prediction

"This project focuses on predicting car prices using machine learning models, specifically linear regression and ridge regression."

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alexsuakim/MachineLearning

Machine learning model implementations from scratch in Python

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khinydnlin/car_auction_price_predictions

ML models to provide estimated car auction prices in Myanmar

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HoldenCole/Commodities-Analysis

Trying to build a repository that can be used to analyze & forecast commodities prices

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leanerr/-Ridge-Regression-interpretation-

Ridge Regression for House details dataset with Turicreate (GraphLab)

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LuluW8071/Laptop-Price-Prediction

A collection of machine learning models for predicting laptop prices

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Denis-Mukhanov/regex_vin

Practicum Workshop

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SteffenMoritz/ridge

CRAN R Package: Ridge Regression with automatic selection of the penalty parameter

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jolars/sgdnet

Fast Sparse Linear Models for Big Data with SAGA

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salimt/Ankara-House-Price-Prediction-Analysis

Predicting house prices using Ridge, SVR, GBR, XGBoost, LightGBM, Random Forest and Stacked CV

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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.

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sumony2j/Linear_Regression

Explore a collection of linear regression projects showcasing implementations and applications in Python. From simple linear regression to advanced techniques like ridge and polynomial regression, this repository offers hands-on examples with diverse datasets.

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rahulvyasm/Medical-Insurance-Cost-Prediction

Predicting Medical Insurance Cost using Machine Learning (Linear Regression, Ridge Regression)

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akashreddy03/image-denoising

This project explores the usage of machine learning techniques in image denoising, particularly ridge regression and dictionary learning. It also includes an implementation of a readily runnable python script for capturing and denoising an image

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sukhmani1303/flight-price-prediction

A Streamlit ML app for flight price prediction. This one project involves concepts like EDA, Linear, Lasso & Ridge Regression, Kfold, Hyper Parameter Tuning & GridSearchCV. I am constantly building a rich repository of all the information I have on a particular topic as part of my machine learning practice. Very Helpful for ML beginners.

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airdipu/Data-Mining-using-R

This project is on Data Mining process using R depending on ISLR book.

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soumizde/insurance_prediction

Insurance Premium Prediction

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bhavesh2205/Real-Estate-Valuation-Prediction-New-Taipei-City

Built a regression model for house price prediction of New Taipei city of Xindian district, Taiwan. which can help urban design and urban policies, as it could help identify what factors have the most impact on property prices.

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alirezarahimi1393/Classical-Learning

Welcome to my Classical Learning Projects repository, where I showcase my work in the fields of supervised and unsupervised learning. Here, you'll find code and datasets for various projects, such as classification and clustering tasks, implemented using popular algorithms like decision trees, neural networks, and k-means.

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fby1997/Lasso-Regression-coordinate-gradient-descent-proximal-gradient-and-ADMM-Ridge-Regression

Use Ridge Regression and Lasso Regression in prostate cancer data

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NishraMahveen/Medical-Insurance-Cost-Analysis

In this project, I analyzed the Medical Insurance Charges Dataset as part of the "Data Analysis with Python" course by IBM. This project involved examining the data to understand patterns and factors influencing medical insurance charges.

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MohammadYasinKarbasian/Machine-Learning-Homeworks

This repository contains my solutions and implementations for assignments assigned during the Machine Learning course.

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mllite/sklearn2json

A tool to jsonify some sklearn and xgboost models. A high level serialization that works across programming languages. Used for mllite tests.

Language: Python - Size: 117 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

aptech/gauss-glmnet

A GAUSS wrapper of the glmnet package for fitting generalized linear models via penalized maximum likelihood.

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daemonX10/Linear-Ml-Model-algerian-forest

This Model Build using Linear Regression + Ridge for Fire weather index on Algerian Data set

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MdTanvirHossainTusher/Indian-Movie-Rating-Prediction

The model is capable of predicting the ratings of movies

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DataScienceVishal/Marketing_vs_Sales

Ridge and Lasso demonstration on small dataset

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DataScienceVishal/Car_Price_Prediction

Car Price Prediction using Linear Regression, Ridge Regression and Lasso Regression

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rajadevineni/Applied_Machine_Learning

This repository consists of the homework assigments as part of BUAN 6341 - Applied Machine Learning. The solutions in this repository are to be used strictly as reference purposes only. Copying or submitting the same solutions found in this repository violates the UTD honor code

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nancyagrwal/Machine-Learning

CS 6140: Machine Learning Assignments

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BenjaminRueling/Car-Price-prediction

Linear regression models on a Car-Price Dataset

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VamshiTeja/ML-Algorithms

Implementation of some Machine Learning Algorithms from scratch

Language: Python - Size: 1.57 MB - Last synced at: over 1 year ago - Pushed at: over 6 years ago - Stars: 4 - Forks: 0

shubhankar90/custom-ml-implementaion

Code store for custom implementation of some machine learning algorithms from scratch.

Language: Python - Size: 11.7 KB - Last synced at: over 1 year ago - Pushed at: over 7 years ago - Stars: 1 - Forks: 0

SBalas/Machine-Learning-Models

knn, Regression (LASSO, Ridge), Logistic, Principal component Analysis (PCA), Discriminant Analysis (LDA, QDA), Trees, Random Forest, Boosting

Language: Jupyter Notebook - Size: 24 MB - Last synced at: about 1 month ago - Pushed at: over 3 years ago - Stars: 2 - Forks: 0

siddharthp30/ridge-regression-house-price-prediction

Implementing Ridge Regression (L2) to predict House Prices

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ali101298/Loan-interest-rate-prediction

Predicting loan interest rate using R.

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DominikHommer/MachineLearning

Discover the power of Machine Learning through practical projects in our dynamic repository. Dive into linear models, classification techniques, and more, with projects ranging from spam detection to fruit classification. Perfect for learners at all levels, our repository grows with new insights and applications. Stay tuned for continuous updates!

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shinho123/Boston-house-price-prediction

2022년 1학기 데이터처리언어 팀 프로젝트 : 보스턴 집값 예측 문제

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damaniayesh/Insurance_Regression_Prediction

The project provides a Regression on the Insurance Prediction Data which shows the features of individuals, tuned using Ridge & Lasso.

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vaibhavdangar09/YES_BANK_STOCK_CLOSING_PRICE

Yes-Bank-Stock-Closing-Price-Prediction refers to a type of project or task in the field of data science and machine learning that involves developing predictive models to estimate the Closing Price of stock

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Faroja/Machine-Learning-Practice-5

Practice Machine Learning Model Complexity in Linear Model

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