COLLEGE PRESENTATION

Stock-Trend Prediction

Name:Sojitra Kartik Batch:A5 Roll No:141 Branch:CE Enrollment No:21002170110187

Description

The stock trend prediction system is a machine learning-based application that uses data analysis and predictive algorithms to forecast the future trends of stocks. The system is built using Django, a Python web framework, which provides a robust and scalable platform for data processing and analysis. This project aims to use historical stock data to inform future predictions. It utilizes Django and Python to analyze and visualize the data, providing insights into trends and patterns that can be used for predictive modeling.

introduction

Python libraries like Pandas, NumPy, and Matplotlib will be used for data analysis and visualization. Django will be used for web development, including user authentication and data management. Django web framework for backend development. Python machine learning libraries for stock trend prediction. HTML, CSS, and JavaScript for frontend development. corsheadres,rest-framework,numpy,matplotlib,sklearn,sslserver,tensorflow

Technical Implementation

Data collection: Historical stock data will be collected from various sources, including Yahoo Finance and Alpha Vantage. Data preprocessing: The collected data will be preprocessed to remove missing values, normalize the data, and prepare it for training the machine learning models. Feature engineering: Features will be extracted from the preprocessed data, including technical indicators and market sentiment data. Model training: Machine learning models, such as decision trees, random forests, and neural networks, will be trained on the preprocessed data to predict stock trends. Web application development: A web application will be developed using Django to provide a user interface for users to interact with the machine learning models and view the predictions.

Implementation Details

THANK YOU

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