Update README.md

README.md

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 # Content Table
 
 - [Stock-Predictor-V4](#stock-predictor-v4)
-  - [What inspired you to create a Stock-Predictor for your School project?](#what-inspired-you-to-create-a-stock-predictor-for-your-school-project)
-  - [How did I narrow down the topic for my Stock-Predictor project and what is the structure of my work?](#how-did-i-narrow-down-the-topic-for-my-stock-predictor-project-and-what-is-the-structure-of-my-work)
   - [1a. Installation](#1a-installation)
   - [1b. Installation with Virtual Environment (venv) in Python](#1b-installation-with-virtual-environment-venv-in-python)
   - [2. Data Preparation](#2-data-preparation)
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-## What inspired you to create a Stock-Predictor for your School project?
-As a technology enthusiast, I am constantly on the lookout for innovative ideas to work on and expand my skillset. Therefore, I decided to explore the field of AI and its potential applications in finance.
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-Motivated by my curiosity and passion for technology, I embarked on a project to create a Stock-Predictor V4. This project involves using advanced algorithms and machine learning techniques to analyze historical data and predict future stock market trends.
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-By building this project, I hope to contribute to the development of new AI-based solutions that can assist investors and traders in making informed decisions. I am excited to explore this fascinating area of technology and see what possibilities it holds.
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-## How did I narrow down the topic for my Stock-Predictor project and what is the structure of my work?
-To narrow down the topic for my Stock-Predictor project, I first researched various areas in finance where AI technology could be applied. After careful consideration, I decided to focus on stock market prediction as it is a crucial aspect of finance that can benefit greatly from AI-based solutions.
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-My project aims to examine the accuracy and effectiveness of different machine learning models in predicting stock prices. To achieve this, I collected and analyzed historical stock market data and used it to train and test various models, including linear regression, random forest, and neural networks.
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-The structure of my work involves several key components, including data preprocessing, model selection, feature engineering, and performance evaluation. I first preprocess the data by cleaning and formatting it for analysis. Next, I select the most suitable machine learning model based on its performance metrics. Then, I engineer relevant features to improve the model's accuracy. Finally, I evaluate the performance of the model based on various metrics such as accuracy, precision, and recall.
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-Overall, my Stock-Predictor project is structured in a way that allows me to systematically examine and represent the effectiveness of AI-based solutions in predicting stock prices.
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 ## 1a. Installation
 ```
 git clone https://github.com/Qerim-iseni09/Stock-Predictor-V4.git