AB
abdulrahimzulfiqar/Stock-Price-Prediction-Using-Scikit-Learn-Linear-Regression
This project predicts the next day's stock closing price using Lag Features + Linear Regression (with scikit-learn). Built with public financial data from Yahoo Finance and visualized with cost convergence and prediction analysis.
📈 Stock Price Prediction Using Scikit-Learn Linear Regression
This project demonstrates how to predict the next day's stock closing price using supervised learning with Linear Regression from scikit-learn. We use public stock data from Yahoo Finance, engineer lag-based features, and evaluate the model's performance with error metrics and visualizations.
🧠 Overview
- ✅ Downloaded historical stock data (Apple Inc.) using
yfinance - ✅ Created lag features from past 5 days to use as inputs
- ✅ Applied feature scaling using
StandardScaler - ✅ Trained a Linear Regression model using
sklearn - ✅ Evaluated the model using MSE and R²
- ✅ Visualized actual vs predicted prices
- ✅ Built entirely in Google Colab using free tools
🗂️ Project Structure
| File | Description |
|---|---|
stock_prediction_sklearn.ipynb |
Full notebook with training, evaluation, and plots |
README.md |
Project documentation (this file) |
images/ |
 |
📊 Data Source
- Ticker:
AAPL(Apple Inc.) - Timeframe: Jan 2020 to Jan 2024
- Source: Yahoo Finance
⚙️ Technologies Used
- Python
- Google Colab
- Pandas, NumPy
- Matplotlib
- scikit-learn
- yfinance
📉 Model Details
- Input features: Closing prices of the last 5 days
- Target: Next day's closing price
- Model:
sklearn.linear_model.LinearRegression - Metrics:
- Mean Squared Error (MSE)
- R² Score
📈 Results
- Prediction error is relatively low, and the model captures general trends.
- Perfect accuracy is not expected as stock markets are affected by external, unmodeled factors.
🖼️ Visualizations