The objective of this project is to predict the volatility of Yahoo's stock prices using historical data. The dataset contains various features including opening price, closing price, adjusted closing price, high, low, and more. However, the focus will be on utilizing the closing price information for volatility prediction.
Objectives
Data Exploration and Visualization: Understand the dataset through summary statistics and visualizations to identify patterns and relationships.
Data Preprocessing: Clean and preprocess the data to handle missing values, encode categorical variables, and normalize numerical features.
Feature Engineering: Create new features that might improve the model's predictive power.
Model Building: Train and evaluate different machine learning models to predict stock price volatility.
Model Evaluation: Assess the models using appropriate metrics and select the best-performing model.
By systematically exploring, preprocessing, and modeling the data, this project aims to develop a robust predictive model for stock price volatility and provide valuable insights into the factors driving market fluctuations.
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Overview
The objective of this project is to predict the volatility of Yahoo's stock prices using historical data. The dataset contains various features including opening price, closing price, adjusted closing price, high, low, and more. However, the focus will be on utilizing the closing price information for volatility prediction.
Objectives
Data Exploration and Visualization: Understand the dataset through summary statistics and visualizations to identify patterns and relationships. Data Preprocessing: Clean and preprocess the data to handle missing values, encode categorical variables, and normalize numerical features. Feature Engineering: Create new features that might improve the model's predictive power. Model Building: Train and evaluate different machine learning models to predict stock price volatility. Model Evaluation: Assess the models using appropriate metrics and select the best-performing model.
Use Case
Financial Forecasting Risk Management Trading Strategy Development Portfolio Management Market Analysis
Benifits
Improved Accuracy of Volatility Predictions Enhanced Decision-Making Economic Gains Increased Market Understanding Risk Mitigation
By systematically exploring, preprocessing, and modeling the data, this project aims to develop a robust predictive model for stock price volatility and provide valuable insights into the factors driving market fluctuations.