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This repository contains a collection of hacks and tips for feature engineering. It is a great resource for anyone who wants to learn how to improve the performance of their machine learning models.
Use advanced feature engineering strategies and select best features from your data set with a single line of code. Created by Ram Seshadri. Collaborators welcome.
Crafting static and dynamic models for data exfiltration detection via DNS traffic analysis. Static model trained on batch data, while dynamic model simulates a continuous stream. Rigorous analysis, feature engineering, and model training conducted. Implementation part of AI for Cyber Security Master's assignment at the University of Ottawa, 2023.
This framework is a versatile toolkit for data analysis across domains, offering robust data processing, feature selection, predictive modeling, and visualization tools adaptable to various datasets.
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.
Consider a real estate company that has a dataset containing the prices of properties in the Delhi region. It wishes to use the data to optimise the sale prices of the properties based on important factors such as area, bedrooms, parking, etc.
The Bike-Sharing Demand Prediction Project aims to develop a predictive model to estimate the demand for shared bikes in the American market for BoomBikes, a bike-sharing provider looking to accelerate revenue post the Covid-19 pandemic. The project involves thorough data exploration and preprocessing.