Forecasting Netflix Customer Retention based on Gaussian Process Regression

This project is a part of research on Breast Cancer Diagnosis with Machine Learning algorithm using data-driven approaches. The final outcomes of the research were later published at an IEEE Conference and added to IEEE Xplore Digital Library.

It is From Analytics Vidhya Hackathons, Sponsored by Club Mahindra. It is based on Regression Problem, Where Accuracy matters the most, It is measured by RMSE Score. Different Techniques such as Stacking, Ensembling, Boosting and Scientific Operations such box-cox Operations to reduce skewness of the data.

Bank Institution Term Deposit Predictive Model

Exploratory Data Analysis (EDA) is one of the techniques used for extracting vital features and trends used by machine learning and deep learning models in Data Science

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

Desafio de clusterização de clientes feito para o IFood e Tera. Utilizando as bibliotecas Plotly, Sklearn e Yellowbrick conseguimos fazer a clusterização em 3 dimensões de forma eficiente e visual utilizando as features construídas no feature engineering a partir de bases de clientes, pedidos e sessões do iFood.

Action recognition using LSTM

Boston house price prediction using Linear Regression.

StableStockPredictor is a robust deep learning model for predicting S&P 500 stock prices, built with TensorFlow and Keras. It leverages LSTM networks with gradient clipping, robust scaling, and stable feature engineering (e.g., RSI, moving averages, volatility) to ensure reliable performance in volatile markets.

Detecting ideal clusters from imdb's movie dataset to segment using unsupervised learning