RoyWeiiiii/Scope_2_A-cutting-edge-digital-approach-for-rapid-C-phycocyanin-detection-in-Spirulina-platensis

Evaluate the robustness and performance between ML and DL models in predicting the CPC concentration under various image capturing devices, types of input image datasets, and lighting conditions. The findings in our current study can overcome the bottleneck by eliminating the need for laborious manual extraction processes and reducing the time and

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NeuroByte-Consulting/Sales-Data-Analysis-and-Forecasting-Using-Ensemble-Methods

Sales Data Analysis and Forecasting Using Ensemble Methods. Data Collection and Cleaning, Exploratory Data Analysis, Data Analysis and Data Visualization, and Sales Forecasting

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Mehrab-Kalantari/Multi-Modal-House-Price-Estimation

House price estimation from visual and textual features using both machine learning and deep learning models

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owenl0000/HousePricesProject

Kaggle Project

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Thiraput01/CIBMTR-Equity-in-post-HCT-Survival-Predictions

Notebooks for Kaggle competition

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jofaval/xgboost-cheatsheet

Quick cheatsheet about XGBoost, a Gradient Boosted regularized technique published in 2014

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dyavadi8769/Student_Performance_Indicator_AWS_CI-CD

This repository contains a machine learning model aimed at predicting student performance across various metrics. Utilizing a diverse set of Machine Learning Regression algorithms, the model predicts scores based on demographic and academic variables.This project demonstrates robust approach to leveraging machine learning for educational outcomes.

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METALXRAY/House-Price-Prediction-using-Machine-Learning-with-Python

Prediciting the Prices of House using the Boston House Price Dataset by applying the XGBoost Regressor Model

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rajeevvhanhuve/Mercedes-Benz-Greener-Manufacturing

Machine Learning

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DataScienceVishal/Boston_House_Price_Prediction

Predicting house prices in Boston using the XGBoost regressor model.

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OmerGamie/mlproject

This repo hosts an end-to-end machine learning project designed to cover the full lifecycle of a data science initiative. The project encompasses a comprehensive approach including data Ingestion, preprocessing, exploratory data analysis (EDA), feature engineering, model training and evaluation, hyperparameter tuning, and cloud deployment.

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Rahulgarg95/fitbit-calorie

A regression model to predict calories burnt using values from multiple sensors.

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Sahaj-26/House-Price-Prediction

The project aims to predict house prices in California based on various features using machine learning techniques. It uses the California housing dataset, comprising 20640 data entries and 8 attributes, with the target being the house price.

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Mehrab-Kalantari/Advanced-House-Price-Prediction

Predicting house prices using advanced regression algorithms

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YomnaAhmed97/Head-Pose-Estimation

Worked on AFLW2000-3D dataset which is a dataset of 2000 images. The regression model of predicting the 3 angles (pitch - yaw - roll) of head pose estimation was XGboost Regressor.

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juliorodrigues07/datachallenge

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raghavendra17/Cab-Booking-Prediction

Build a Machine Learning model to predict the total count of cabs booked in each hour by the new data. Research on Cyclic features

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prajwalDU/Bike-sharing-demand-prdiction-prajwal

Bike Sharing Demand Prediction By Supervised Machine Learning Algorithms Implementation On Seoul Bike Sharing Dataset

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Yoojin730/DataModeling_forRightofMobility

2022-01 데이터마이닝이론및응용 프로젝트 <장애인 이동권 제고를 위한 콜택시 이용편의 증진 방안 : 서울특별시를 중심으로>

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PriyeshDave/Site-Energy-Intensity-Predictor

In this project, a regression-based performance prediction model was developed to estimate building energy consumption based on simplified façade attribute information and weather conditions.

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prateekagr21/Predicting-Sales-of-Video-Games

Doing Analysis of the sales of video games across the globe and predicting the sales using various Machine Learning Algorithms

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prateekagr21/Black-Friday-sales-prediction

Analysis and prediction of the sales data during Black Friday sale using some Machine Learning Algorithms.

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