My solution to House-Prices Advanced Regression Techniques, A beginner-friendly project on Kaggle.
Project 2 Group C - Predicting FinTech Bootcamp Graduate Salaries
It was a competition on KAGGLE for prediction on the most sales products on bikes via their features
Genetic assignment of individuals to known source populations using network estimation tools.
Metis project 2/7
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.
Predicting house price
This is a project developed as part of the Foundations of Data science course at New York University
Regression Machine Learning Project
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.
A repository containing machine learning projects and models.
"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.
Built a Gradient Boosting model by employing Lasso Regularization and Hyper-parameter tuning
Kaggle challenge asking to predict the final price of each home based on their description/properties.
Advanced Regression model on Housing Data from Australia for my Upgrad - IIITB AI ML PG Course
NYU CSCI-GA 3033 Final Project
A series of Statistical Modelling assignments with the use of R. Applications of Linear, Polynomial, Logistic and Poisson Regression in various datasets
Practical Implementation of Linear Regression on Boston Housing Price Prediction
Your all-in-one Machine Learning resource – from scratch implementations to ensemble learning and real-world model tuning. This repository is a complete collection of 25+ essential ML algorithms written in clean, beginner-friendly Jupyter Notebooks. Each algorithm is explained with intuitive theory, visualizations, and hands-on implementation.
ML | Regression Analysis| Random Forest| XGBoost| Gradient Boost| EDA| Feature Engineering| Feature selection
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