Learn how to predict stock prices using Python in our comprehensive course. This guide will cover the basics of stock price prediction, using historical data to forecast future trends.
Course Overview
In this course, you will:
- Understand the fundamentals of stock price data and financial markets.
- Learn to preprocess and clean financial data using Python.
- Explore various machine learning algorithms for stock price prediction.
- Implement a real-world stock price prediction model.
- Evaluate and optimize your model for better accuracy.
Key Topics
Data Collection
To start your journey in stock price prediction, you need to collect historical stock price data. We provide a step-by-step guide on how to fetch and store this data using Python.
Learn more about data collection
Data Preprocessing
Data preprocessing is a crucial step in the stock price prediction process. This section will teach you how to clean and preprocess financial data using Python libraries like Pandas and NumPy.
Read more about data preprocessing
Machine Learning Algorithms
We will explore various machine learning algorithms suitable for stock price prediction, such as linear regression, decision trees, and neural networks.
Discover machine learning algorithms for stock price prediction
Model Evaluation
Evaluating your model's performance is essential for understanding its accuracy and reliability. This section will cover techniques for model evaluation, including metrics like Mean Absolute Error (MAE) and R-squared.
Case Study
In our case study, you will apply the knowledge gained from the course to predict stock prices for a popular company. This hands-on project will help you solidify your understanding of the concepts covered.
Keep an eye on the market and stay ahead with our comprehensive stock price prediction course. Enroll now and start your journey towards becoming a data science expert!