This assignment is a programming assignment wherein we have to build a multiple linear regression model for the prediction of demand for shared bikes.

HDB flats resale price prediction. Neural network in Python. Machine learning models in R. Data pre-processing, feature engineering and feature selection mainly in R.

Building logistic classifier model (RFE)

Student grade prediction using different machine learning models

Feature Selection Examples

This is a project demonstrating Logistic Regression method using Python. An education company named X Education sells online courses to industry professionals. On any given day, many professionals who are interested in the courses land on their website and browse for courses.

A US-based housing company named Surprise Housing has decided to enter the Australian market. The company uses data analytics to purchase houses at a price below their actual values and flip them on at a higher price.

Explored data using data visualisation and exploratory data analysis. Used Logistic Regression to create a basic prediction model. Improved model using recursive feature elimination.

We are required to build a regression model using regularization in order to predict the actual value of the prospective properties and decide whether to invest in them or not.

Objective: To reduce customer churn, telecom companies need to predict which customers are at high risk of churn.

A comprehensive ML framework to detect heart disease using the Cleveland dataset

Bank Customer Behaviour Prediction

Data warehouse and analytics project to predict bike theft prediction from TPS data

This project tackles BoomBikes' post-Covid revenue decline by predicting shared bike demand after the lockdown. A consulting company identifies key variables impacting demand in the American market. The goal is to model demand, aiding BoomBikes in adapting its strategy to meet customer expectations and navigate market dynamics.

Building predictive models to detect and prevent the fraudulent transactions happening on cerdit cards and debit cards. Implementation of 2nd factor authentication for safe and secure transactions.

This project demonstrates building a classification model for imbalanced data. Feature engineering, feature selection and extensive EDA. Comparing of logistic regression, random forest and ADA Boost models are done before finalizing the best model.

[Features extraction method] You can find the new version of CASTOR_KRFE at https://github.com/bioinfoUQAM/CASTOR_KRFE

Linear Regression, how number of features affect outcome

Trabajo fin de máster de ciencia de datos UC-UIMP-CSIC 2018-2019