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long-short-term-memory

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Marmara Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği bölümünde sunulan “DERİN SİNİR AĞLARI İLE FEDERE ÖĞRENME TABANLI BİR KÖTÜ AMAÇLI YAZILIM TESPİT UYGULAMASI” başlıklı tez çalışmasına ait kaynak kodlarıdır.

  • Updated Jul 16, 2024
  • Python

The purpose of this project is to predict stock prices using historical data and financial indicators with LSTM networks. It involves data extraction, cleaning, model training, and an interactive app for predictions. This tool aids financial analysts, portfolio managers, and investors in making informed decisions.

  • Updated Jul 10, 2024
  • Jupyter Notebook

This repository contains analysis and predictive modeling of household electricity consumption using Python. It includes data cleaning, exploratory data analysis (EDA), time series forecasting (ARIMA, SARIMA, LSTM), and model evaluation to optimize energy usage.

  • Updated Jul 6, 2024
  • Jupyter Notebook

Repository contains my Jupyter Notebook files (ran either in VSCode using the Jupyter Notebook extension, either Notebook or Lab through Anaconda, or Google Colab) for a Recurrent Neural Network (RNN) regressor model that predicts energy demand in t-horizon, for EEL6812 - Advanced Topics in Neural Networks (Deep Learning with Python) course, PRJ03

  • Updated Jul 3, 2024
  • Jupyter Notebook

Stock Trend Prediction with LSTM is a powerful tool designed to empower users with insights into the dynamic world of stock market trends. Leveraging cutting-edge technologies such as Long Short-Term Memory (LSTM) networks and real-time data from Yahoo Finance, this project enables users to forecast future price movements of stocks with precision.

  • Updated Jun 12, 2024
  • Jupyter Notebook

Project aims to forecast potato prices in India using LSTM, KNN, and Random Forest Regression, integrating historical data on prices, regional stats, and rainfall patterns. Targeting agricultural stakeholders for informed decision-making.

  • Updated Jun 1, 2024
  • Python

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