Telecommunication Company Churn Project

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

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

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

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

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

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

Feature transformation is a technique in machine learning that changes the way features are represented in order to improve the performance of machine learning algorithms. This can be done by transforming the features to a different scale, removing outliers, or creating new features from existing

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

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

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

Exploratory Data Analysis for HR dataset

Bank Customer Behaviour Prediction

Time Series Model

Cloud image generation with Python and OpneCV

Aircraft Engine Run-to-Failure Simulation

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.

Stock price prediction is the process of forecasting future stock prices based on historical data and market indicators.