My solution to House-Prices Advanced Regression Techniques, A beginner-friendly project on Kaggle.

Project 2 Group C - Predicting FinTech Bootcamp Graduate Salaries

Laptop price prediction model using XGB, Ridge, Lasso and SciKit-learn's Linear Regression. In the end, I deployed the best one using Joblib and Gradio.

Regression Machine Learning Project

Genetic assignment of individuals to known source populations using network estimation tools.

Predicting house price

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.

"Learning R for data scientists." This phrase describes the process of acquiring the skills and knowledge necessary to use the R programming language for data analysis.

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

Kaggle challenge asking to predict the final price of each home based on their description/properties.

Metis project 2/7

NYU CSCI-GA 3033 Final Project

Practical Implementation of Linear Regression on Boston Housing Price Prediction

ML | Regression Analysis| Random Forest| XGBoost| Gradient Boost| EDA| Feature Engineering| Feature selection

Analysis and Modelling data from King county housing data

Built a Gradient Boosting model by employing Lasso Regularization and Hyper-parameter tuning

A repository containing machine learning projects and models.

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.

In this project, we will predict the price for AMES House and learn Machine Learning Algorithms, different data preprocessing techniques such as Exploratory Data Analysis, Feature Engineering, Feature Selection, Feature Scaling and finally to build a machine learning model.