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+# Financial News Sentiment and Stock Market Correlation Analysis
+
+This project focuses on the detailed analysis of financial news sentiment and its correlation with stock market movements. By leveraging natural language processing (NLP) techniques, sentiment analysis, and advanced financial analytics, this analysis aims to uncover meaningful insights that can enhance financial forecasting accuracy. The ultimate goal is to develop innovative strategies that utilize news sentiment as a predictive tool for stock market trends.
+
+## Table of Contents
+
+1. [Project Overview](#project-overview)
+2. [Business Objective](#business-objective)
+3. [Dataset Overview](#dataset-overview)
+4. [Tasks and Deliverables](#tasks-and-deliverables)
+5. [Installation](#installation)
+6. [Usage](#usage)
+7. [Contributing](#contributing)
+8. [License](#license)
+9. [Acknowledgements](#acknowledgements)
+
+## Project Overview
+
+This project focuses on analyzing a large corpus of financial news data to discover correlations between news sentiment and stock market movements. It encompasses data engineering, financial analytics, and machine learning engineering to enhance predictive analytics capabilities at Nova Financial Solutions.
+
+## Business Objective
+
+Nova Financial Solutions aims to enhance its predictive analytics capabilities to significantly boost its financial forecasting accuracy and operational efficiency through advanced data analysis. This involves:
+- Performing sentiment analysis on financial news headlines.
+- Establishing statistical correlations between sentiment scores and stock price movements.
+- Providing actionable insights and investment strategies based on the analysis.
+
+## Dataset Overview
+
+The Financial News and Stock Price Integration Dataset (FNSPID) contains:
+- **headline**: Title of the news article.
+- **url**: Direct link to the full news article.
+- **publisher**: Author/creator of the article.
+- **date**: Publication date and time.
+- **stock**: Stock ticker symbol.
+
+## Tasks and Deliverables
+
+### Week 1:
+
+- **Task 1: Exploratory Data Analysis (EDA)**
+  - Descriptive Statistics
+  - Text Analysis (Sentiment Analysis & Topic Modeling)
+  - Time Series Analysis
+  - Publisher Analysis
+
+- **Task 2: Quantitative Analysis using PyNance and TA-Lib**
+  - Load and prepare stock price data
+  - Apply technical analysis indicators
+  - Visualize the data
+
+- **Task 3: Correlation Between News and Stock Movement**
+  - Align datasets by dates
+  - Perform sentiment analysis on news headlines
+  - Calculate daily stock returns and correlation with sentiment scores
+
+### Deliverables:
+- **Interim Report**: Summary of initial findings and partial progress (max 3 pages).
+- **Final Report**: Detailed analysis and insights (up to 10 pages).
+
+## Installation
+
+1. **Clone the Repository**:
+   ```bash
+   git clone https://github.com/yourusername/your-repository.git
+   cd your-repository