Stock Market Analysis Prediction

🔹 Goal

Analyze historical stock data to predict future prices using machine learning techniques.

📊 Dataset

• Kaggle Stock Market Analysis Data
• Features: Date, Open, High, Low, Close, Volume

🛠️ Implementations

• Data Collection and Preprocessing
• Exploratory Data Analysis (EDA)
• Feature Engineering
• Model Selection and Training
• Evaluation and Predictions

🚀 Getting Started

3. Download the dataset

Download the dataset from Kaggle and place it in the data/ folder.

4. Run the notebook script

Open https://github.com/RebeccaMorolong/stockpredictionanalysis.ipynb/tree/main in Jupyter Notebook or JupyterLab and follow along!

🧰 Requirements

• Python 3.8+
• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn
• statsmodels
• prophet

(See requirements.txt for full details.)

📈 Results

The notebook demonstrates:

• Data cleaning and visualization
• Feature engineering for time series
• Linear Regression and Random Forest models
• Model evaluation using RMSE and R² metrics

🤝 Contributing

Contributions are welcome! Please open an issue or submit a pull request.

📄 License

This project is licensed under the MIT License.

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Happy Analyzing!

1. Clone the repository