This repository contains code and resources for predicting stock market trends using machine learning algorithms. The goal is to analyze historical stock data and make predictions for future price movements.
- Data Collection: Gathered historical stock data from Apple stock data from kaggle
- Exploratory Data Analysis (EDA): Analyzed key metrics and patterns in the data.
- Feature Engineering: Created relevant features for model training.
- Machine Learning Models: Implemented and trained machine learning models for stock prediction.
- Evaluation: Assessed model performance using Accuracy score , prediction value , MSE
- Visualization: Visualized predictions and trends.
- data/: Contains the dataset used for training and testing.
- notebooks/: Jupyter notebooks for EDA and model development.
- src/: Source code for the machine learning models.
- results/: Visualizations and model evaluation results.
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Clone the repository:
bash git clone https://github.com/yourusername/stock-market-prediction.git cd stock-market-prediction
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Install dependencies:
bash pip install -r requirements.txt
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Run the notebooks in the notebooks/ directory for data analysis and model development.
- Jupyter Notebook
- Sk Learn
- python
- Matplotlib
Provide instructions on how to use the trained model for predictions.
Include visualizations, metrics, and insights gained from the project.
- Sudharsan N