Data-Science-Financial-News-Project

📰 Financial Market News Sentiment Analysis

Analyse financial news headlines using Python, NLP, and Machine Learning to infer market sentiment.

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🔍 Project Overview

This project scrapes and processes financial news headlines, applies NLP techniques to perform sentiment analysis, and explores how sentiment relates to market trends and investment decisions.

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🚀 Features

• Text Preprocessing: Cleans headlines by removing noise, tokenising, and normalising.
• Sentiment Classification: Implements models (e.g., RandomForest Classification) to predict the accuracy, F1 Score.

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🛠️ Tech Stack

• Python 3.x
• Libraries: pandas, numpy, scikit-learn, TensorFlow, Keras, transformers, Matplotlib

🛠 Install & Setup

1. Clone the repository

   git clone https://github.com/GeekyVishweshNeelesh/Python-Machine-Learning-Project.git
   cd Python-Machine-Learning-Project

2. Create and activate virtual environment

   python3 -m venv venv
   source venv/bin/activate       # macOS/Linux
   venv\Scripts\activate        # Windows

📓 Usage

1. Open Project_Python_Financial_Market_News_Sentimental_Analysis.ipynb in Jupyter.
2. Go through each section:
   • Data loading & cleaning
   • Exploratory Data Analysis (EDA)
   • Preprocessing
   • Training & evaluating models
   • (Optional) Market sentiment correlation
3. Execute all cells in order. Modify the data source or extend to live news feeds if needed.

🤝 Contributing

Feel free to:

• Fork the repo
• Submit pull requests with improvements (e.g., model enhancements, code cleanup, dockerization)
• File bug reports or feature requests via GitHub Issues

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📝 Author

Vishwesh Neelesh – Aspiring Data Scientist
GitHub: GeekyVishweshNeelesh
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