This repository contains introductory notebooks for forecasting

Forecast the CocaCola prices data set. Prepare a document for each model explaining how many dummy variables you have created and RMSE value for each model. Finally which model you will use for Forecasting.

Time series forecasting on Superstore sales data using ARIMA and Holt–Winters models. Includes data preprocessing, decomposition, model comparison, and 12-month sales forecast. Holt–Winters (Multiplicative Trend) selected for best accuracy.

Unsupervised customer segmentation on the UCI Online Retail II dataset using RFM features and KMeans. Includes full pipeline: data cleaning, feature engineering, outlier handling, model selection (Elbow & Silhouette), and actionable segment insights for targeted marketing.

Data Science: Machine Learning analysis of B2B website Visits and Purchase Patterns

As séries temporais são usadas para analisar tendências, padrões e sazonalidades nos dados ao longo do tempo, bem como para prever eventos futuros com base nos dados históricos. Para a análise de série temporal deste DataFrame utilizei a técnica de Suavização Exponencial com o modelo SimpleExpSmoothing.