Project Report: Stock Price Prediction

Project Report Link

Stock Price Prediction

This repository contains a Jupyter Notebook focused on stock price prediction, utilizing machine learning techniques.

Features

Data preprocessing and cleaning.
Exploratory Data Analysis (EDA) with visualizations.
Machine learning model training and evaluation.
Performance metrics for model assessment.

Technologies Used

Jupyter Notebook
Python
Pandas, NumPy for data manipulation
Matplotlib, Seaborn for visualization
Scipy for statistical analysis
Scikit-learn for machine learning

Algorithms & Models Used

Linear Regression (from scikit-learn) for stock price prediction.
Train-Test Split (80% training, 20% testing) for model evaluation.
Performance Metrics:
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- R² Score

Usage

Modify and run code cells for different analyses.
Update markdown cells for documentation purposes.
Export the notebook to HTML or PDF as needed.

Contact

For any inquiries, feel free to reach out via GitHub Issues.