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My solution to House-Prices Advanced Regression Techniques, A beginner-friendly project on Kaggle.
Regression Machine Learning Project
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.
Genetic assignment of individuals to known source populations using network estimation tools.
Predicting house price
Project 2 Group C - Predicting FinTech Bootcamp Graduate Salaries
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.
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.
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.
A series of Statistical Modelling assignments with the use of R. Applications of Linear, Polynomial, Logistic and Poisson Regression in various datasets
"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.
ML | Regression Analysis| Random Forest| XGBoost| Gradient Boost| EDA| Feature Engineering| Feature selection
Kaggle challenge asking to predict the final price of each home based on their description/properties.
NYU CSCI-GA 3033 Final Project
Metis project 2/7
Built a Gradient Boosting model by employing Lasso Regularization and Hyper-parameter tuning
Analysis and Modelling data from King county housing data
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.
Repository consisting of the notebooks I worked on to submit my courseworks at Royal Holloway
A repository containing machine learning projects and models.
This is a project developed as part of the Foundations of Data science course at New York University
Advanced Regression model on Housing Data from Australia for my Upgrad - IIITB AI ML PG Course
Practical Implementation of Linear Regression on Algerian Forest Fire Dataset.
Practical Implementation of Linear Regression on Boston Housing Price Prediction
Predictive Analytics for Real Estate Investment: A Regression Model Approach for Surprise Housing in the Australian Market using Regularization methods (Ridge and Lasso)
House-Prices Advanced Regression Techniques Competition Solution
Final Group Project for Advanced Data Science for Public Policy @ McCourt
Repository about the projects in the course of Modeling and control of cyberphysical systems at PoliTo in 2022/2023
The "Car Price Prediction" project focuses on predicting the prices of cars using machine learning techniques. By leveraging popular Python libraries such as NumPy, Pandas, Scikit-learn (sklearn), Matplotlib, Seaborn, Lasso regression, and Linear regression, this project provides a comprehensive solution for accurate price estimation.
A Machine Learning exploration evaluating various models to predict Airbnb prices, culminating in an optimized Gradient Boosting Regressor.
A summative coursework for MAS8404 Statistical Learning for Data Science
Developed regularization and tree-based machine learning models to predict remission status in a cohort of 5059 patients. Elastic net and Random Forest models were compared on F1 scores accuracy, sensitivity, specificity, and AUC ROC.
Regression models(lasso, ridge, DT) using NumPy.
In this project, I build 20+ models predicting Spotify song popularity. These include neural networks, Lasso and Ridge regression models. I also leverage OpenAI chat-completion API to engineer features from song lyrics.
Approach to some basic Machine Learning Techniques.
Built a regression model to predict university admission using linear, polynomial, and regularized regression techniques (lasso, ridge, and elastic net) and achieved 98% accuracy.