Gemstone Price Prediction - End to End ML Project with AWS deployment

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.

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.

In this series of notebooks, we will dive into each step of the data analysis process of a data set with some information about a list of cars and several attibutes, including their prices. So essentially we will develop a model to predict cars price.

This is First Project of Machine Learning by me

Metis project 2/7

Model Building and Testing using Ridge, Lasso and ElasticNet Methods

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

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

A small project addressing a regression problem explains implementation of multiple linear regression techniques, hyperparameter tuning, collinearity, model overfitting and complexity using LASSO, Ridge and Elastic net

Data Models in R for Multiple Linear Regression and three models (Ridge, Lasso, and Elastic-Net), to predict Medicare claim costs of Type 2 diabetes patients with other diagnoses. We used Data from Entrepreneur’s Medicare Claims Synthetic Public Use Files (DE-SynPUFs) for our analysis.

Sub-seasonal temperature and heatwave prediction in Central Europe with AI (linear and random forest machine learning models)

Practical Implementation of Linear Regression on Algerian Forest Fire Dataset.

Practical Implementation of Linear Regression on Boston Housing Price Prediction

Forest Fire Data

End-to-end machine learning regression model for predicting housing prices in Bengaluru, with Heroku deployment.

Approach to some basic Machine Learning Techniques.

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

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