My solution to House-Prices Advanced Regression Techniques, A beginner-friendly project on Kaggle.
Genetic assignment of individuals to known source populations using network estimation tools.
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.
Metis project 2/7
Regression Machine Learning Project
A repository containing machine learning projects and models.
House-Prices Advanced Regression Techniques Competition Solution
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.
Predicting house price
Nextflow pipeline integrating TCGA multi-omics with MOFA and downstream LASSO to reveal tissue-specific oncogene selection pressures
"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
Practical Implementation of Linear Regression on Boston Housing Price Prediction
NYU CSCI-GA 3033 Final Project
Advanced Regression model on Housing Data from Australia for my Upgrad - IIITB AI ML PG Course
Repository about the projects in the course of Modeling and control of cyberphysical systems at PoliTo in 2022/2023
ML | Regression Analysis| Random Forest| XGBoost| Gradient Boost| EDA| Feature Engineering| Feature selection
A series of Statistical Modelling assignments with the use of R. Applications of Linear, Polynomial, Logistic and Poisson Regression in various datasets
It was a competition on KAGGLE for prediction on the most sales products on bikes via their features
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.
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