The findings in the present study will be a breakthrough for the estimation of CPC concentration from S. platensis solely based on the information provided in the image without the need to perform a prior extraction process and identification of CPC concentration using analytical equipment.

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

Boston house price prediction using Linear Regression.

This project aims to develop a machine learning model to predict bike-sharing demand based on various factors such as weather conditions, time of day, and historical usage patterns. The dataset used for this project consists of 8760 records and 14 attributes.

Predict stock Closing price for the next day

Predicted the sales price using different machine learning prediction models for Housing Prices Dataset

- Notebook for car price prediction using regression models.

Predicting diamond prices using various regression algorithms. This project involves data preprocessing, feature engineering, and model evaluation to determine the best predictor for diamond prices.

Covered Advanced Machine Learning topics like hyperparameter tuning for Machine Learning techniques, GridSearchCV, Pruning, Linear SVM Regressor, Polynomial Kernel SVM Regressor, Bayesian Network Analysis etc,.

Built neural networks (NNs) and Regression models for supervised learning. The NN task is formulated as multi-class classification problem for hand-written images, and the goal is to model the relationship between an image’s content and label. Also uses knowledge on Regression models to predict housing prices in Boston to develop Machine Learnin…

The goal of this project is to predict the demand for Seoul's Bike Sharing Program. A dataset (8760,14) containing historical usage patterns, such as temperature, time, and other relevant data is used to build a regression model.

This repository contains implementations of popular machine learning algorithms including Support Vector Machine (SVM), Decision Tree, and Naive Bayes. Each algorithm is implemented separately, providing clear and concise examples of their usage for classification tasks.

Prediction of intensity or degree of the emotion of the given text.

Predicting sales volume for different product types using multiple regression and analyzing the impact customer reviews have on sales.

Goal is to find the Prediction of Protein content, so used All Regression Algorithms of Machine Learning

Regression algorithms to predict the minimum temperature

This repository contains code for predicting stock prices using various machine learning models. The models implemented include Linear Regression, SVM Regression, KNN Regression, Kernel Ridge Regression, and Ridge Regression.

Selection of the best model for the pricing of the houses located in wide parts of Bengaluru .