Implementation of algorithms such as normal equations, gradient descent, stochastic gradient descent, lasso regularization and ridge regularization from scratch and done linear as well as polynomial regression analysis. Implementation of several classification algorithms from scratch i.e. not used any standard libraries like sklearn or tensorflow.

Capstone project_1 include python code for best fit regression model, SQL feature store and tableau dashboard

This project for price prediction of Real Estate Property. Model is trained on dataset with Property Price as the Target Variable. The model takes into account the important factors

This repository contains topics and codes related to Machine Learning and Data Science, especially in Python

Complete Data Science Concepts, is an ongoing collection of my work as I progress through my Data Science journey. Starting from the fundamentals, I will be sharing projects, codes, and techniques that I learn along the way. It's designed to be useful for both beginners and advanced learners.

Showcasing Simple Linear, Multiple Linear, Polynomial and Ridge Regression on Insurance Cost Dataset to predict insurance price. Also, I have generated a report using Quarto.

This repository is the third project of the master's degree in AI Engineering that I am following. It aims toto optimize real estate price valuation through the use of advanced regularisation techniques in linear regression models by implementing Lasso, Ridge and Elastic Net in order to obtain accurate and stable price predictions.

Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.

The objective of this project is to study the COVID-19 outbreak using basic statistical techniques and make short term predictions using ML regression methods.

A MATLAB package for modelling multivariate stimulus-response data

The project aims to predict NYC taxi trip durations using advanced regression techniques. We utilized Polynomial Linear Regression, Ridge Regression, and Lasso Regression for feature extraction and achieved a validation R² score of 0.67. Feature engineering included KMeans clustering, Haversine distance calculation, and date-time feature extraction

Machine Learning Algorithms

Bu proje, Kontr firmasının borsa verilerini kullanarak hisse senedi fiyatlarının gelecekteki değerlerini tahmin etmeye yönelik gelişmiş makine öğrenimi modelleri içerir. Farklı algoritmalarla performans analizi yaparak yatırım kararlarını destekleyici öngörüler sağlar.

A machine learning application aimed at predicting employee salaries based on various features such as experience, education level, location, etc. By using different models and techniques, the project seeks to present an optimized model for salary predictions.

Second practical assignment for the course "I302 - ML and Deep Learning". The work consists of three regression problems where different models are designed from scratch.

Echo State Networks for Time Series Forecasting

Ridge Estimation of Vector Auto-Regressive (VAR) Processes

Slides for the 26th International Conference on Computational Statistics (COMPSTAT 2024)

In this project, we implement a linear regression model and its extensions on a student grades dataset to enhance performance. The workflow includes advanced EDA, data preprocessing, and assumption checks. Key steps: dataset overview, univariate and bivariate analysis, data preprocessing, model building(2nd degree,l1,l2,EN) and result visualization

Prediction Using Linear Regression Models of Least Squares, Ridge Regression, and Lasso Regression