Extensive EDA of the IBM telco customer churn dataset, implemented various statistical hypotheses tests and Performed single-level Stacking Ensemble and tuned hyperparameters using Optuna.
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Updated
Nov 9, 2021 - HTML
Extensive EDA of the IBM telco customer churn dataset, implemented various statistical hypotheses tests and Performed single-level Stacking Ensemble and tuned hyperparameters using Optuna.
Explainable AI for Healthcare
Participated in Analytics Vidya Hackathon ( JOB-A-THON | May 2021 ). This Repository contains all code, reports and approach.
Traveling Ionospheric Disturbances Forecasting System, funded by the European Community, Horizon Europe
The goal of the project is to build a predictive model using machine learning concepts to predict customer attrition for a telecom service company.
"Wind Power Predictor" is a machine learning project that forecasts turbine output using real-time data from Turkish wind farms. Its web app interface offers convenient access to predictions, enabling informed decisions for maximizing energy production and advancing renewable energy usage.
A diverse dataset comprising various car attributes such as mileage, model year, brand, and more, our predictive model employs to accurately forecast the prices of audi car. From data preprocessing to model training and evaluation, our repository provides code implementation, enabling users to understand and replicate our results seamlessly.
Predicting Kolonials service time to improve their route planner
We build a model to predict the value of used cars, while also considering speed and quality of the prediction.
The telecom operator Interconnect would like to forecast churn of their clients. To ensure loyalty, those who are predicted to leave will be offered promotional codes and special plans.
Create a model that can accurately predict whether a user belongs to the HCP(Healthcare Professional) category or not. Based on server logs.
Smart-Industrial-Predictive-Solutions Feynn Labs Internship project
Проекты, выполненные в рамках образовательной программы "Специалист по Data Science" АНО ДПО "Образовательные технологии Яндекса"
Create a machine learning model to determine the likelihood of a customer defaulting on a loan based on credit history, payment behavior, and account details.
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