The goal of our analysis was to use different time series methods to predict the oil price for the last 6 months of the data, September 2017 through February 2018, and determine the best prediction model for this data.
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.
Elevating Times Series Forecasting of Cars, Truck Sales in UK for precise and competitive forecasting in diverse industries.
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.
Auto ARIMA Model
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.
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