Telecommunication Company Churn Project
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Updated
Jun 9, 2021 - Jupyter Notebook
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
Artificial Neural Network using Keras in python to identify customers who are likely to churn.
Data Set: House Prices: Advanced Regression Techniques Feature Engineering with 80+ Features
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.
Exploratory Data Analysis for HR dataset
Bank Customer Behaviour Prediction
This repository demonstrates the scaling of the data using Scikit-learn's StandardScaler, MinMaxScaler, and RobustScaler.
Time Series Model
Aircraft Engine Run-to-Failure Simulation
Cloud image generation with Python and OpneCV
Created machine learning models capable of classifying candidate exoplanets from a raw dataset.
Linear Regression+Decision Tree+Random Forest
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