huseyincenik/data_science

Data Science materials

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kev-nat/Wheat-Production-and-Demand-Forecasting

This is our final project for Data Science and Applications Course

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zrkhadija/Multivariate-sector-price-prediction-using-macroeconomic-indicators

This project focuses on Multivariate Sector Price Prediction using macroeconomic indicators. By leveraging a custom-built LSTM model in PyTorch, we predicted the prices of 10 financial sectors simultaneously. The model takes as input the historical price data of these sectors along with key macroeconomic indicators.

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CD-AC/DataScience-Sales_Prediction

This project utilizes Prophet, a powerful forecasting tool developed by Facebook, to predict seasonal sales patterns. Leveraging time series analysis techniques, the project aims to forecast the seasons of the year with the highest sales.

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steveee27/Time-Series-Stock-Price-Prediction-using-LSTM-Model

This project implements a LSTM (Long Short-Term Memory) model to predict stock prices of AAPL (Apple Inc.) and AMD (Advanced Micro Devices) using historical data. The dataset includes stock prices with features like Open, High, Low, Close, Adjusted Close, and Volume. The model is trained using LSTM to learn the temporal dependencies in the data.

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steveee27/Financial-Transaction-Balance-Prediction-using-Time-Series

This project predicts financial account balances using time series forecasting and machine learning models. It trains models on transaction data to forecast future balances, using features like time of transaction and past balances.

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marziehmirzaei/EnergyPrediction-Arima-Transformer

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RPf357/Group-4-Time-Series-Forecasting

Store sales forecasting using time series forecasting is a data-driven approach that utilizes historical sales data to predict future sales trends. By analyzing patterns, seasonality, and other temporal factors in the data, businesses can make informed decisions about inventory management, staffing, and marketing strategies.

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