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The workflow covers data preprocessing, MinMax scaling, temporal train-test splitting, and next-day target shifting. Three models—Linear Regression, Support Vector Machine (with grid search hyperparameter tuning), and a two-hidden-layer Keras MLP with early stopping—are trained and evaluated with MSE, MAE, and R-squared. Preliminary results highlight strong accuracy and model-dependent performance differences.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/predicting-stock-prices-using-different-machine-learning-and-deep-learning-models-gc-511-stock-price-forecasting-presentation/126725/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/predicting-stock-prices-using-different-machine-learning-and-deep-learning-models-gc-511-stock-price-forecasting-presentation/126725.png","ImageObject",300,407,{"name":92,"@type":93},"Ava Thompson","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-04","2026-08-05",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the prediction target in the project?","Question",{"text":112,"@type":113},"The target is the next day’s closing stock price. The series is shifted by one time step to create a “Target” column for forecasting.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which models are compared for predicting Amazon stock prices?",{"text":117,"@type":113},"The project compares Linear Regression, Support Vector Machine (SVM), and a Multi-Layer Perceptron (MLP) built with Keras.",{"name":119,"@type":110,"acceptedAnswer":120},"How are model performance and errors evaluated?",{"text":121,"@type":113},"Performance is assessed using regression metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R²) score.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},126725,1785934425,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":81},962084925782,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Predicting Stock Prices Using Different Machine Learning and Deep Learning Models  \nGC-511  \nPresenters: Afnan Crystal, Rohith Sundar, and Hrithik Singh | Advisor: Dr. Mahmut Karakaya  \nCollege of Computing and Software Engineering, Kennesaw State University  \nIntroduction  \n• Our project focuses on predicting daily closing prices and stock movements of Amazon, a dynamic corporation known for unpredictable stock prices influenced by various factors  \n• We employ a comprehensive approach, comparing the performance of Linear Regression, Support Vector Machine (SVM), and MultiLayered Perceptron (MLP) models on financial time series data from January 2, 2005, to August 21, 2019  \n• Our goal is to provide accurate forecasts and our project outlines the methodologies and techniques used for stock price forecasting  \n• Preliminary Results: Pilot study shows promising results; we achieved an overall accuracy of 93% on five categories of food.  \nMethod Overview  \n• Data Preprocessing: Firstly, we prepare and clean the dataset and target column to create the prediction target which is the closing price for the following day  \n• Model Selection: We use these three diverse machine learning & deep learning models for our prediction tasks  \ni. Linear Regression (LR)  \nii. Support Vector Machine (SVM)  \niii. Multi-Layered Perceptron (MLP)  \n• Training the Models: The model optimizes SVM through grid search for hyperparameter tuning, requires no tuning for Linear Regression, and implements early stopping in a two-layer MLP to prevent overfitting and enhance generalization  \n• Predictions and Evaluation: Model performance is assessed using standard regression metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R2) score  \nModel Training/Dataset  \nWe generated a \"Target\" column by shifting the stock prices by one time step and applied . MinMax scaling, are within the [0, 1] range  \nclosing  \nensuring values  \nWe split the dataset to use 80% of the data for training and 20% of the data for testing using a train-test split with the shuffle parameter set to False to preserve the temporal order of the data  \nThese preprocessing steps are essential for creating a well-structured, temporally aligned dataset, ensuring effective model training and accurate predictions in the domain of stock price forecasting  \nAlgorithms Deployed  \n• Linear Regression : In this context, it is employed to predict the next day's closing stock price for Amazon . Linear Regression provides clear insights into the relationship between the input features (such as'Close' and 'Google_Trends') and the target variable ('Target' column representing the next day's closing price) . The coefficients in the linear equation signify the impact of each feature on the prediction .  \n• Support Vector Machine: SVM is employed as a regression model to forecast the next day's closing stock price for  \nAmazon . Hyperparameters for the SVM model are optimized using GridSearchCV where the hyperparameters include 'C' (regularization parameter), 'kernel' (type of kernel function), and 'gamma' (kernel coefficient) . SVM complements Linear Regression in this project, offering a more sophisticated approach to capture non-linear relationships, potentially improving predictive accuracy. The grid search optimizes the SVM model for the data, enhancing its  \nperformance in predicting Amazon's stock prices .  \n• Multi-Layered Perceptron (MLP): MLP is constructed and trained to forecast the next day's closing stock price for Amazon . In the project a neural network model is constructed using Keras . It consists of two hidden layers with 50 units and ReLU activation functions . The model is trained using Mean Squared Error (MSE) as the loss function and the Adam optimizer where we used Early stopping to prevent overfitting.  \nResults  \nThe SVM model has the highest MSE, indicating that on average, the squares of the errors are the largest among the three models . However, its MAE is","cbCaicnSLzuhuTC5","https://ap.wps.com/l/cbCaicnSLzuhuTC5","pdf",335353,"English","# Introduction\n# Method Overview\n## Data Preprocessing\n## Model Selection and Training\n## Predictions and Evaluation\n# Model Training/Dataset\n# Algorithms Deployed\n## Linear Regression\n## Support Vector Machine\n## Multi-Layer Perceptron (MLP)\n# Results\n# Conclusion","[{\"question\":\"What is the prediction target in the project?\",\"answer\":\"The target is the next day’s closing stock price. The series is shifted by one time step to create a “Target” column for forecasting.\"},{\"question\":\"Which models are compared for predicting Amazon stock prices?\",\"answer\":\"The project compares Linear Regression, Support Vector Machine (SVM), and a Multi-Layer Perceptron (MLP) built with Keras.\"},{\"question\":\"How are model performance and errors evaluated?\",\"answer\":\"Performance is assessed using regression metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R²) score.\"}]","Predicting Stock Prices Using Different Machine Learning and Deep Learning Models - GC-511 - stock price forecasting presentation | PDF"]