Demo on the capability of Yandex CatBoost gradient boosting classifier on a fictitious IBM HR dataset obtained from Kaggle. Data exploration, cleaning, preprocessing and model tuning are performed on the dataset
Analyze NASDAQ100 stock data. Used ARIMA + GARCH model and machine learning techniques Naive Bayes and Decision tree to determine if we go long or short for a given stock on a particular day
This repo contains step-by-step procedure to predict whether a customer's Loan request is Approval or rejected, Based on the compition on Analytics Vidhya
An Artificial Intelligence based Chat Bot using python tools like Numpy, Pandas, Spacy etc. Counsellor Bot will mimic the characteristics and emotion interpretation skills of human and generate response on basis of emotion of engager.
Goal Using the data collected from existing customers, build a model that will help the marketing team identify potential customers who are relatively more likely to subscribe term deposit and thus increase their hit ratio
Identifies the parts of the Germany population that best describe the core customer base of the Arvato company. Uses a supervised model to predict which individuals are most likely to convert into becoming customers for the company.
In this project, we will apply supervised learning techniques and an analytical mind on data collected for the U.S. census to help CharityML (a fictitious charity organization) identify people most likely to donate to their cause.