This repository contains a Jupyter Notebook that demonstrates the application of linear regression models to time series data, specifically on stock market data (S&P 500). The notebook walks through the steps of preprocessing the data, fitting a linear regression model, and visualizing the results, including trends and uncertainty (standard deviations).
- Data Preprocessing: The dataset is cleaned and processed for use in linear regression modeling.
- Modeling: Linear regression models are applied to identify trends in the time series data.
- Visualization: The notebook provides visual representations of the actual stock prices, predicted trends, and standard deviations to assess model performance.
- Python 3.x
- Jupyter Notebook
- Required Python packages:
numpypandasmatplotlibscikit-learn
Install the required packages using the following command: bash pip install numpy pandas matplotlib scikit-learn
- Clone this repository: bash git clone
Open the notebook in Jupyter: jupyter notebook Linear\ Regression\ Models.ipynb
Run the cells to preprocess the data, train the linear regression model, and visualize the results.
The model provides a clear representation of the trend in the stock market data, along with upper and lower bounds representing one standard deviation from the predicted trend. A red dashed line is used to depict the trend, and green dashed lines are used to represent the confidence intervals (±1 standard deviation).