BasicPythonStockPricePredictor

Starter MLRM model using python. Sample stock price dataset from Nishanth Shetty on Kaggle.

Stock Price Prediction using Linear Regression

Project Overview

This project aims to forecast the future closing price of a stock using a Multiple Linear Regression model. The analysis is based on historical price data and technical indicators like moving averages. This project serves as a practical application of time-series analysis, feature engineering, and machine learning for financial data.

Dataset

The dataset used is the historical daily price data for the stock EW-MAX.csv. It contains Date, Open, High, Low, Close, Adj_Close, and Volume columns, spanning over 4000 trading days.

Methodology

1. Data Cleaning: The raw data was loaded, dates were converted to the correct datetime format, and the Date column was set as the index.
2. Feature Engineering: Two key features were created to capture market trends: the 7-day Moving Average (MA_7) and the 30-day Moving Average (MA_30).
3. Model Training: A Multiple Linear Regression model was trained on the first 80% of the historical data. The features used were Adj_Close, MA_7, and MA_30, with the target being the next day's closing price.
4. Evaluation: The model's performance was evaluated on the remaining 20% of the data (the test set). The Root Mean Squared Error (RMSE) was the primary metric.

Results

The model achieved an RMSE of 1.60 on the test set, which was significantly lower than the stock's standard deviation of 30.0, indicating a strong predictive performance for a baseline model.

How to Run

1. Ensure you have Python and Jupyter Notebook installed.
2. Install the required libraries: pip install pandas scikit-learn matplotlib statsmodels
3. Clone this repository.
4. Open and run the PriceProjection.ipynb notebook.