House-Prices Advanced Regression Techniques Competition Solution

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

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.

A collection of multiple projects involving tasks such as classification, time series forecasting , regression etc. on a number of datasets using different machine learning algorithms such as random forest, SVM, Naive Bayes, Ensemble, perceptron etc in addition to data cleaning and preparation.

Repository about the projects in the course of Modeling and control of cyberphysical systems at PoliTo in 2022/2023

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.

Approach to some basic Machine Learning Techniques.

A repository containing machine learning projects and models.

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

A Machine Learning exploration evaluating various models to predict Airbnb prices, culminating in an optimized Gradient Boosting Regressor.

Final Group Project for Advanced Data Science for Public Policy @ McCourt

Built a regression model to predict university admission using linear, polynomial, and regularized regression techniques (lasso, ridge, and elastic net) and achieved 98% accuracy.

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

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

Regression models(lasso, ridge, DT) using NumPy.

A summative coursework for MAS8404 Statistical Learning for Data Science

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.

"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.

ols_regression, Simple_Linear_Regression,univariate_Polynomial_Regression,Bayesian_Regression

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.