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README.md

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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.
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+# 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.
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 # Financial News Sentiment and Stock Market Correlation Analysis - Task 1
 
 This branch focuses on **Task 1: Exploratory Data Analysis (EDA)**, where we perform initial analysis on the financial news dataset to uncover insights and set the foundation for further analysis.
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+> > > > > > > task-1
 
 ## Table of Contents
 
 1. [Project Overview](#project-overview)
 2. [Business Objective](#business-objective)
 3. [Dataset Overview](#dataset-overview)
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 4. [Tasks and Deliverables](#tasks-and-deliverables)
 5. [Installation](#installation)
 6. [Usage](#usage)
 7. [Contributing](#contributing)
-8. [License](#license)
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-4. [Task 1 Overview](#task-1-overview)
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+8. # [License](#license)
+9. [Task 1 Overview](#task-1-overview)
+   > > > > > > > task-1
 
 ## Project Overview
 
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 ## 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:
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 - 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:
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 - **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.
 
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 ## Tasks and Deliverables
 
 ### Week 1:
 
 - **Task 1: Exploratory Data Analysis (EDA)**
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   - Descriptive Statistics
   - Text Analysis (Sentiment Analysis & Topic Modeling)
   - Time Series Analysis
   - Publisher Analysis
 
 - **Task 2: Quantitative Analysis using PyNance and TA-Lib**
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   - Load and prepare stock price data
   - Apply technical analysis indicators
   - Visualize the data
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   - Calculate daily stock returns and correlation with sentiment scores
 
 ### Deliverables:
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 - **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/dagiteferi/Financial-News-Sentiment-Stock-Market-Correlation-Analysis.git
    cd Financial-News-Sentiment-Stock-Market-Correlation-Analysis
 
+   ```
+
 2. **Install Dependencies**:
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 ```bash
 pip install -r requirements.txt
 ```
+
 ** Contributing**
 Contributions are welcome! Please fork the repository and use a feature branch. Pull requests are warmly welcomed.
 
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    ```
 7. **Create a Pull Request**: Go to the repository on GitHub, switch to your branch, and click the `New Pull Request` button. Provide a detailed description of your changes and submit the pull request.
 
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 **License**
 Distributed under the MIT License. See LICENSE for more information.
 
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 ## Task 1 Overview
 
 ### Task 1: Exploratory Data Analysis (EDA)
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 ### Deliverables for Task 1:
 
 - **EDA Report**: Summary of initial findings and insights from the exploratory data analysis.
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