Stock Price Predictor
A machine learning model that predicts stock price trends using financial datasets, regression algorithms, and comprehensive feature engineering for accuracy analysis.
The Problem
Stock market price prediction is inherently complex due to the high volatility and numerous influencing factors. Building a reliable baseline model requires careful feature engineering, data preprocessing, and rigorous model evaluation.
The Solution
Using Scikit-learn regression models trained on financial datasets processed with Pandas and NumPy, this project builds a pipeline from raw data ingestion through feature engineering to prediction output — with detailed accuracy evaluation.
Key Features
- Financial data preprocessing pipeline
- Feature engineering from OHLCV data
- Multiple regression model training
- Prediction accuracy analysis
- Model performance evaluation metrics
- Data visualization of predictions vs actuals
Technical Highlights
- Used Pandas and NumPy for complex data transformation and cleaning
- Trained and compared multiple Scikit-learn regression models
- Performed comprehensive model evaluation with MAE, RMSE, and R² metrics
- Built in Jupyter Notebook with clear documentation and visualizations
Challenges Faced
- 01
Handling missing data and outliers in financial time series datasets
- 02
Feature engineering that captures meaningful patterns without overfitting
- 03
Selecting the right regression algorithm based on dataset characteristics