{"repo":"gamzeakkurt/deep-learning-stock-prediction","free":true,"listed":false,"github":"https://github.com/gamzeakkurt/deep-learning-stock-prediction","clone":"git clone https://github.com/gamzeakkurt/deep-learning-stock-prediction.git","description":"This project uses machine learning models (Linear Regression and LSTM) to analyze and forecast stock market prices. It retrieves stock data from Yahoo Finance, performs exploratory data analysis (EDA), processes and engineers features, and predicts future prices. The project includes model evaluation metrics","language":"Python","stars":24,"topics":["applestockmarket","lstm","lstm-neural-networks","stock-market","stock-price-prediction","arima","chatgpt","deep-learning","economics","finance"],"license":"MIT","category":"machine-learning","readme_excerpt":"📊 Yahoo Finance Stock Market Analysis (AAPL, MSFT, AMZN, TSLA) 🚀 Introduction Stock market prediction is a crucial area in financial analysis. Prices of stocks are influenced by various factors, such as market trends, economic indicators, and investor sentiment. This project focuses on analyzing and forecasting stock prices of Apple (AAPL), Microsoft (MSFT), Amazon (AMZN), and Tesla (TSLA) using deep learning. Using Yahoo Finance data , we apply Exploratory Data Analysis (EDA), Feature Engineering, Preprocessing, and Long Short-Term Memory (LSTM) Regression modeling to predict stock prices. The notebook file and update other models are available at: Kaggle Notebook --- 🎯 Objectives ✅ Retrieve stock market data using yfinance 📈 ✅ Perform EDA to visualize trends & correlations 📊 ✅ Extracting features like RSI, MACD,Bollinger Bands, Moving Averages vs. ✅ Preprocess the data for deep learning (normalization, handling missing values) ✅ Implement an LSTM and Linear Regression models for stock price forecasting 🧠 ✅ Evaluate predictions using RMSE, and MAE scores ✅ Compare actual vs. predicted stock prices 📉 --- 🏗️ Project Workflow 🔹 Step 1: Data Collection (Yahoo Finance API) 🔹 Step 2: Exploratory Data Analysis (EDA) 🔹 Step 3: Feature Engineering (Technical Indicators) 🔹 Step 4: Data Preprocessing (Normalization, Reshaping) 🔹 Step 5: LSTM & Linear Regression Models Training & Prediction 🔹 Step 6: Model Evaluation (Error Metrics) 🔹 Step 7: Results & Visualization --- ⚙","default_branch":null,"files":null,"tree":[],"storefront":"/r/gamzeakkurt","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/gamzeakkurt/deep-learning-stock-prediction/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}