Collection of end-to-end regression problems (in-depth: linear regression, logistic regression, poisson regression) 📈

Using PyCaret to Predict Apple Stock Prices

Textual analysis of Investment themed subreddits to predict future stock returns

Data Mining project. In this project I tried to predict the number of likes of the videos through some machine learning models.

Examine the features of used cars posted on CarGurus at different times and locations to predict the selling price

My capstone project for Ryerson's Data Analytics, Big Data, and Predictive Analytics. The project I worked on was based on an open competition project called DengAI: Predicting Disease Spread. The objective of the project was to predict the total number of cases in the testing set of Iquitos, Peru & San Juan, Puerto Rico.

In this project, we will evaluate the performance and predictive power of a model that has been trained and tested on data collected from homes in suburbs of Boston, Massachusetts.

Regression Model to predict the estimated hospitalisation for a patient to test a new drug for Diabetes using EHR data

Welcome to my Data Science Portfolio! I'm Saravanan — an IITM Pravartak Certified Data Science Professional with practical experience building end-to-end ML and Deep Learning solutions across healthcare, finance, e-commerce along with NLP and GEN AI Domains. This portfolio highlights my work on real-world data-driven projects.

This repository contains files on Predict probability whether a given blight ticket will be paid

Determine concrete compressive strength

This repository consists of all the assignments i have done as a part of the neural networks lab which was a part of my curriculum.

In the project we built the following algorithms : Decision Tree Classifiier and Regressor, AdaBoost Classifiier , Gradient Boost Regressor

MLFND boston housing prices prediction project.

Analyzed the crime events that occurred in the City of Chicago, from 2001 till January 2017, using the data provided from Chicago Police Department’s CLEAR (Citizen Law Enforcement Analysis and Reporting) System

An R Companion for the Applied Linear Statistical Models by Kutner, Nachtsheim, Neter, and Li (2005).

To find the strength of the concrete-Regression Problem

A data-based approach to analyse and compare prices and characteristics of Airbnbs listings in Montreal and Toronto and identify the key factors affecting their prices.