Netflix Stock Price Prediction
- Kyuta Yasuda
- Jan 28
- 2 min read
Project Description
This project involves predicting the stock prices of Netflix using historical stock data. By implementing machine learning models, the project aims to analyze historical trends and provide predictions to assist in informed decision-making. The focus was on building robust predictive models using algorithms such as XGBoost.
Key Highlights
Objective: Predict Netflix's stock prices using historical data.
Dataset: Historical Netflix stock prices, including features like Open, High, Low, Close, and Volume.
Model: XGBoost Regressor for time series prediction.
Tools and Libraries:
Python
Libraries: pandas, scikit-learn, XGBoost, matplotlib
Jupyter Notebook for implementation.
Project Title: Netflix Stock Price Prediction
Project Description
This project involves predicting the stock prices of Netflix using historical stock data. By implementing machine learning models, the project aims to analyze historical trends and provide predictions to assist in informed decision-making. The focus was on building robust predictive models using algorithms such as XGBoost.
Key Highlights
Objective: Predict Netflix's stock prices using historical data.
Dataset: Historical Netflix stock prices, including features like Open, High, Low, Close, and Volume.
Model: XGBoost Regressor for time series prediction.
Tools and Libraries:
Python
Libraries: pandas, scikit-learn, XGBoost, matplotlib
Jupyter Notebook for implementation.
Workflow
Data Preprocessing:
Imported and cleaned the stock dataset.
Engineered features based on stock attributes (e.g., percentage change, moving averages).
Split the data into training and testing sets.
Model Implementation:
Used XGBoost Regressor, a gradient boosting algorithm optimized for regression tasks.
Tuned hyperparameters such as learning rate, number of estimators, and maximum depth to improve performance.
Training and Evaluation:
Trained the model on the training data and evaluated predictions on the test set.
Plotted actual vs. predicted stock prices to visualize model performance.
Achieved a Mean Absolute Error (MAE) of $3.25 on the test set.
Visualization:
Plotted predicted vs. actual stock prices to assess prediction accuracy.
Results
Model Performance:
Mean Absolute Error (MAE): $3.25
Learning Outcomes
Gained practical experience with XGBoost for regression tasks.
Enhanced understanding of feature engineering for financial datasets.
Developed a workflow for handling time series data and evaluating model predictions.
Learned to visualize and interpret model outputs effectively.
Next Steps
Experiment with additional algorithms like LSTM or ARIMA for time series forecasting.
Perform further hyperparameter tuning using techniques like Grid Search or Bayesian Optimization.
Explore using external datasets (e.g., economic indicators, social sentiment) to enhance predictive accuracy.
Implement real-time prediction capabilities with updated stock data.
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