Artificial Neural Network using Keras in python to identify customers who are likely to churn.

Created machine learning models capable of classifying candidate exoplanets from a raw dataset.

This repository demonstrates the scaling of the data using Scikit-learn's StandardScaler, MinMaxScaler, and RobustScaler.

Exploratory Data Analysis for HR dataset

Build a machine learning model to predict if a credit card application will get approved.

Aircraft Engine Run-to-Failure Simulation

Final Cybersecurity ML project of Marc Mestre and Yana Veitsman for Data Mining and Machine Learning course at University of Valencia, Spring 2021

Telecommunication Company Churn Project

Predictive model that tells important factors(or features) affecting the demand for shared bikes

Cloud image generation with Python and OpneCV

In this project we will apply Recurrent Neural Network (LSTM) which is best suited for time-series and sequential problem, we will be creating a LSTM model, train it on data and make predictions to check its performance.

Forecasting time series data using ARIMA models. Used covariance matrix to find dependencies between stocks.

This repository contains clustering techniques applied to minute weather data. It contains K-Means, Heirarchical Agglomerative clustering. I have applied various feature scaling techniques and explored the best one for our dataset

This project is based on a classification algorithm i.e. Naive Bayes which is run on a mobile dataset consisting of 2000 rows and 15 columns. It is a multi-class problem where mobile phones are classified in accordance with their price range. There are four classes of price ranging from 0 to 3, 0 indicating cheaper mobiles phones and 3 represent…

Data Set: House Prices: Advanced Regression Techniques Feature Engineering with 80+ Features

Time Series Model

Wrangled real estate data from multiple sources and file formats, brought it into a single consistent form and analysed the results.