Job-A-thon ML challenge
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Updated
Jun 27, 2023 - Jupyter Notebook
Job-A-thon ML challenge
The use of Machine Learning Regression models for predicting energy loads of buldings.
Prediction model for Delivery Time by Simple Linear Regression
This repository is about Analysis of Cricket Chirps, Brain-Body Weight, and Salary Discrimination Data: Linear regression, visualization,R2 squared and correlation assessments.
Flight fare Prediction end-to-end project
House Price prediction in US
Prediction model for profit of 50 startups dataset by Multiple Linear Regression
The dataset used for this project is taken from the official UCI Machine Learning Repository.
K-nearest neighbors (KNN) classification is a non-parametric supervised machine learning algorithm. It is a simple yet powerful algorithm that can be used for a variety of classification problems
Life expectancy, an estimate of the number of remaining years of life a person has, is an important consideration for making clinical decisions in primary care. Predicting Life Expectancy helps analyze the average lifespan of the countrymen which helps in making crucial health decisions.
Prediction model for Salary Hike by Simple Linear Regression
📗 This repository provides an in-depth exploration of the predictive linear regression model tailored for Jamboree Institute students' data, with the goal of assisting their admission to international colleges. The analysis encompasses the application of Ridge, Lasso, and ElasticNet regressions to enhance predictive accuracy and robustness.
Exploratory Data Analysis & predicting medical insurance cost with machine learning.
This project provides a performance evaluation of credit card default prediction. Thus different models are used to test the variable in predicting the credit default and we found Random Forest Classifier performs the best with a recall of 0.95 on the test set.
A car price prediction model based on regressor task.
Diamond Price Predictor - Web Application: Predict diamond prices using various regression models: Linear Regression, Lasso, Ridge, ElasticNet, Decision Tree Regressor, Random Forest Regressor, and KNeighbors Regressor. The chosen Random Forest Regressor, with a remarkable accuracy of 97%, is deployed in a user-friendly Flask app
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