Whenever customers purchase certain products from a store, it is important for the store to understand their buying patterns. This can help stores in better placement of specific products. The way to understand these patterns is called Market Basket Analysis.

Machine learning models

This project focuses on predicting the burned area of forest fires using Long Short-Term Memory (LSTM) neural networks. The LSTM model is trained on historical data to forecast the extent of forest fire damage based on various environmental and meteorological factors.

Developed a predictive model to classify individuals into one of seven weight categories (ranging from insufficient weight to obesity type 3) based on various personal factors using diverse neural network architectures.

Example of classification using the RandomForest algorithm, with visual exploratory analysis using seaborn and matplotlib plots, and data normalization using One-Hot Encoding and StandardScaler. Covered in the datascienceacademy course.

Binary classification algorithm that predicts which passengers are transported to an alternate dimension

This project showcases a Network Intrusion Detection System (NIDS) designed to bolster cybersecurity defenses against evolving threats

Este projeto tem como objetivo construir um modelo de Machine Learning capaz de prever a satisfação de clientes de uma companhia aérea.

This repository explores various techniques for handling categorical variables in data preprocessing, focusing on methods such as one-hot encoding, label encoding, and their applications in machine learning models.

Applied Data Science Project

Spotify Classification Problem 2023

build a models that predicts whether an individual makes over $50,000 per year.

Jazz solo improv AI

Training a model using AutoGluon to predict bike sharing demand

Utilizing Apache Spark & PySpark to analyze a movie dataset. Tasks include data exploration, identifying top-rated movies, training a linear regression model, and experimenting with Airflow.

The feature engineering techniques discussed are - dimensionality reduction(pca), scaling(standard scaler, normalizer, minmaxscaler), categorical encoding(one hot/dummy), binning, clustering, feature selection. These are techniques performed on a dataset consisting of Californian House Prices.

client subsection to a term deposit

Life expectancy data processing

DL projects done in Python