Project overview
In this project, you’ll take on the role of a financial analyst tasked with predicting stock market prices using machine learning. You’ll work with historical S&P 500 data, preparing it for analysis, setting up a target variable, training an initial model, and evaluating performance through backtesting.
By adding additional predictors like rolling averages, you’ll aim to improve the model’s predictive power. This project allows you to apply machine learning to a real-world financial scenario, developing skills in time series data handling, model training, and evaluation – valuable additions to a data science portfolio.
Objective: Use machine learning to predict stock market price movement, evaluating and optimizing model performance to build real-world data science skills.
Key skill required
To complete this project, it's recommended to build these foundational skills in Python
- Manipulating data using Python data structures and control flow
- Working with pandas DataFrames to analyze data
- Performing operations on NumPy arrays
- Applying basic machine learning concepts and models
Projects steps
Step 1: Project Overview
Step 2: Cleaning and Visualizing Our Stock Market Data
Step 3: Preparing Our Target for Machine Learning
Step 4: Training an Initial Machine Learning Model
Step 5: Building a Backtesting System
Step 6: Adding Additional Predictors to Our Model
Step 7: Improving Our Model
Step 8: Next Steps
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