Prediction of Crop Cultivation using Machine Learning

Analyzing a Google Merchandise Store (GStore) customer dataset to predict revenue per customer.

Finding donors using supervised learning

Data Analysis in Spark to Identify Customer Churn for a fictional music service.

This repository is a solution to Analytics Vidhya Practice problem called 'Predicting Loan Status Approval'

Uses ML models to predict the type of crime that is likely to happen at a given time of the day at a specific location on a college campus.

Comparisons of various ML algorithm that detects Diabetic Retinopathy from the data that contains area and perimeter of blood vessel, exudates and micro-aneurysm using Machine Learning.

Experiment sentiment analysis using regular classifiers and deep neural networks then compare the performance

With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, the challenge is predict the final price of each home.

Using the Sklearn classifiers: Naive Bayes, Random Forest, Adaboost, Gradient Boost, Logistic Regression and Decision Tree good success rates are observed in a very simple manner. In this work sensitivity is also considered. Treating each record individually, differences are found in the results for each record depending on the model used, which…

This project is to predict food calories based on their nutrients using Linear Regression as a part of machine learning.

Taking into account that the accuracy of statistical results depend on the accuracy of the input data, not only on the algorithm, a Hastie file has been created in which all the records have the correct class assigned and tests of hit rates and sensitivity have been carried out

Identify employees that are probable to change their jobs using CART modelx.

A multi-class classification problem where the task is to classify a file to one of 9 types of Malware usually found in a Windows system, using information from the raw data and metadata of the file.

Diagnosis of Cancer Using Blood Microbiome Data

Данные проекты были выполнены в ходе обучения в Яндекс.Практикуме по профессии "Специалист по Data Science"

This project is a part of the 1000ML Engineer Initiative and aims to predict football player transfer values using gradient boosting algorithms.

Review for LightGBM paper. Implementation of Gradient Boosting and LightGBM framework on Apple Inc (AAPL) NASDAQ prices dataset.

Predicting mammalian taxonomic order based on ecological, geographic, and life-history traits

This project is about a Bike Rental facility located in South Korea, We built different regression models in order to predict the future demand for the rental bikes depending upon the other conditional and non-conditional features in the dataset.