This project is a collaborative machine learning project focused on predicting stock price trends using historical market data. The aim is to analyze past stock prices and apply machine learning techniques to understand and predict future movements.
This project was developed together as a team and all contributors actively participated in data analysis, model development, and result interpretation.
Stock market prediction is challenging due to uncertainty and market volatility. In this project, we:
- Fetched real-time historical stock data using
yfinance - Analyzed and visualized closing price trends
- Performed data preprocessing and scaling using MinMaxScaler
- Built a Linear Regression model for stock price prediction
- Evaluated model performance using RMSE
- Forecasted the next 30 days of stock prices
| Tool | Purpose |
|---|---|
| Python | Core language |
| yfinance | Fetch live historical stock data |
| Pandas & NumPy | Data processing |
| Matplotlib | Data visualization |
| Scikit-learn | Linear Regression, MinMaxScaler, RMSE |
| Google Colab / Jupyter Notebook | Development environment |
- Data Collection — Downloaded historical AAPL stock data (2015–2024) via yfinance
- Exploratory Data Analysis (EDA) — Visualized closing price trends, checked for nulls
- Preprocessing — Scaled data using MinMaxScaler (0–1 range)
- Dataset Creation — Used a sliding window (timespan) approach to create features
- Train/Test Split — 70% training, 30% testing
- Model Training — Linear Regression on windowed closing price sequences
- Evaluation — Calculated Train RMSE and Test RMSE
- Forecasting — Predicted next 30 days using recursive prediction
- Plotted actual vs predicted prices for both train and test sets
- Achieved low RMSE on test data showing strong trend following
- Generated a 30-day future price forecast with visualization
- All outputs and plots are available inside the notebook
- Set up data pipeline using
yfinancefor live stock data fetching - Handled multi-level column issues from yfinance and fixed preprocessing bugs
- Built and trained the Linear Regression model with sliding window approach
- Implemented MinMaxScaler for normalization and inverse transformation
- Created train/test visualizations and 30-day forecast plots
- Debugged array shape errors in prediction pipeline
- Updated and maintained project documentation
- Implement deep learning models (LSTM, GRU) for better accuracy
- Add technical indicators (RSI, MACD, Bollinger Bands)
- Support multiple stock tickers simultaneously
- Hyperparameter tuning for improved predictions
- Deploy as an interactive web application using Streamlit
This repository represents a joint academic project created through collaboration and shared learning. Individual contributions reflect personal understanding and implementation effort.