diff --git a/AI in Python on Windows/README.md b/AI in Python on Windows/README.md
index 95ad98e..46b7401 100644
--- a/AI in Python on Windows/README.md	
+++ b/AI in Python on Windows/README.md	
@@ -1,113 +1,143 @@
 # AI in Python on Windows
 
-This guide will help you set up a Python environment on a Windows machine to develop and work on AI and Machine Learning projects. We'll walk through setting up Python, installing key AI libraries, and running a simple AI program.  
+This guide will help you set up a Python environment on a Windows machine to develop and work on AI and Machine Learning projects. We'll walk through setting up Python, installing key AI libraries, and running a simple AI program.
 
 ## Table of Contents
+
 1. [Introduction](#1-introduction)
 2. [Prerequisites](#2-prerequisites)
 3. [Setting up Python on Windows](#3-setting-up-python-on-windows)  
-    3.1. [Download Python](#31-download-python)  
-    3.2. [Verify Installation](#32-verify-installation)  
-    3.3. [Install a Virtual Environment (Optional but Recommended)](#33-install-a-virtual-environment-optional-but-recommended)
+   3.1. [Download Python](#31-download-python)  
+   3.2. [Verify Installation](#32-verify-installation)  
+   3.3. [Install a Virtual Environment (Optional but Recommended)](#33-install-a-virtual-environment-optional-but-recommended)
 4. [Installing AI Libraries](#4-installing-ai-libraries)  
-    4.1. [NumPy](#41-numpy)  
-    4.2. [Pandas](#42-pandas)  
-    4.3. [Matplotlib & Seaborn](#43-matplotlib--seaborn)  
-    4.4. [Scikit-learn](#44-scikit-learn)  
-    4.5. [TensorFlow](#45-tensorflow)  
-    4.6. [PyTorch](#46-pytorch)  
-    4.7. [Jupyter Notebook](#47-jupyter-notebook)  
+   4.1. [NumPy](#41-numpy)  
+   4.2. [Pandas](#42-pandas)  
+   4.3. [Matplotlib & Seaborn](#43-matplotlib--seaborn)  
+   4.4. [Scikit-learn](#44-scikit-learn)  
+   4.5. [TensorFlow](#45-tensorflow)  
+   4.6. [PyTorch](#46-pytorch)  
+   4.7. [Jupyter Notebook](#47-jupyter-notebook)
 5. [Running a Simple AI Script](#5-running-a-simple-ai-script)
-6. [AI Libraries Overview](#6-ai-libraries-overview)  
+6. [AI Libraries Overview](#6-ai-libraries-overview)
 7. [Resources](#7-resources)
 
 ## 1. Introduction
-Artificial Intelligence (AI) is revolutionizing numerous industries by allowing computers to learn from data and make decisions without human intervention. Python, with its simplicity and extensive ecosystem of libraries, is the most popular language for AI development. In this guide, you'll learn how to set up and use Python for AI development on a Windows machine.  
 
-## 2. Prerequisites  
-Before you start, ensure that your system meets the following requirements:  
-- **Operating System:** Windows 10 or higher  
-- **Python:** Version 3.8 or higher  
-- **RAM:** At least `8 GB` (more is better for heavy AI tasks)  
-- Internet: Required for downloading libraries and datasets  
+Artificial Intelligence (AI) is revolutionizing numerous industries by allowing computers to learn from data and make decisions without human intervention. Python, with its simplicity and extensive ecosystem of libraries, is the most popular language for AI development. In this guide, you'll learn how to set up and use Python for AI development on a Windows machine.
+
+## 2. Prerequisites
+
+Before you start, ensure that your system meets the following requirements:
 
-Basic knowledge of Python programming is also recommended.  
+- **Operating System:** Windows 10 or higher
+- **Python:** Version 3.8 or higher
+- **RAM:** At least `8 GB` (more is better for heavy AI tasks)
+- **Internet:** Required for downloading libraries and datasets
+
+Basic knowledge of Python programming is also recommended.
 
 ## 3. Setting up Python on Windows
-### 3.1. Download Python:  
+
+### 3.1. Download Python
 
 - Visit the official [Python website](https://www.python.org/downloads/) and download the latest stable version for Windows.
 - Make sure to select the option to add Python to PATH during installation.
 
-### 3.2. Verify Installation: 
-Open the Command Prompt (search for `cmd` in the Start menu) and type:  
+### 3.2. Verify Installation
+
+Open the Command Prompt (search for `cmd` in the Start menu) and type:
 
 ```bash
 python --version
 ```
 
-You should see the installed Python version.  
+You should see the installed Python version.
+
+### 3.3. Install a Virtual Environment (Optional but Recommended)
+
+Virtual environments allow you to isolate your project dependencies. To install:
 
-### 3.3. Install a Virtual Environment (Optional but Recommended):  
-Virtual environments allow you to isolate your project dependencies. To install:  
 ```bash
 python -m venv ai_env
 ```
-Activate the environment:  
+
+Activate the environment:
+
 ```bash
 ai_env\Scripts\activate
 ```
-To deactivate the environment, simply run:  
+
+To deactivate the environment, simply run:
+
 ```bash
 deactivate
 ```
 
 ## 4. Installing AI Libraries
-With Python set up, the next step is to install essential AI and machine learning libraries. Below is a list of commonly used libraries and their installation commands:  
 
-### 4.1. NumPy:
-Provides support for large, multi-dimensional arrays and matrices.  
+With Python set up, the next step is to install essential AI and machine learning libraries. Below is a list of commonly used libraries and their installation commands:
+
+### 4.1. NumPy
+
+Provides support for large, multi-dimensional arrays and matrices.
+
 ```bash
 pip install numpy
 ```
-### 4.2. Pandas:
+
+### 4.2. Pandas
+
 A powerful data manipulation library.
+
 ```bash
 pip install pandas
 ```
 
-### 4.3. Matplotlib & Seaborn:
+### 4.3. Matplotlib & Seaborn
+
 For data visualization.
+
 ```bash
 pip install matplotlib seaborn
 ```
 
-### 4.4. Scikit-learn:  
-For classical machine learning algorithms.  
+### 4.4. Scikit-learn
+
+For classical machine learning algorithms.
+
 ```bash
 pip install scikit-learn
 ```
 
-### 4.5. TensorFlow:  
-A popular deep learning framework.  
+### 4.5. TensorFlow
+
+A popular deep learning framework.
+
 ```bash
 pip install tensorflow
 ```
 
-### 4.6. PyTorch:  
-Another leading deep learning library.  
+### 4.6. PyTorch
+
+Another leading deep learning library.
+
 ```bash
 pip install torch torchvision torchaudio
 ```
 
-### 4.7. Jupyter Notebook:  
- To create and share documents containing live code, equations, visualizations, and narrative text.  
- ```bash
- pip install notebook
+### 4.7. Jupyter Notebook
+
+To create and share documents containing live code, equations, visualizations, and narrative text.
+
+```bash
+pip install notebook
 ```
 
 ## 5. Running a Simple AI Script
-Once you've installed the libraries, you can run a simple AI script. Here's an example using a simple neural network with TensorFlow.  
+
+Once you've installed the libraries, you can run a simple AI script. Here's an example using a simple neural network with TensorFlow.
+
 ```python
 import tensorflow as tf
 from tensorflow.keras import layers
@@ -139,30 +169,35 @@ model.fit(x_train, y_train, epochs=5)
 model.evaluate(x_test, y_test, verbose=2)
 ```
 
-Save this script as `mnist_example.py` and run it using the Command Prompt:  
+Save this script as `mnist_example.py` and run it using the Command Prompt:
+
 ```bash
 python mnist_example.py
 ```
 
-You should see the model training and evaluating on the MNIST dataset.  
+You should see the model training and evaluating on the MNIST dataset.
 
 ## 6. AI Libraries Overview
-- **NumPy:** Used for numerical computations and managing arrays. Essential for handling large datasets.  
 
-- **Pandas:** For data manipulation and analysis. Particularly useful for handling structured data like CSV files.  
+- **NumPy:** Used for numerical computations and managing arrays. Essential for handling large datasets.
 
-- **Matplotlib & Seaborn:** Libraries for data visualization, enabling you to plot and analyze data effectively.  
+- **Pandas:** For data manipulation and analysis. Particularly useful for handling structured data like CSV files.
 
-- **Scikit-learn:** A comprehensive library for classical machine learning models like linear regression, clustering, etc.  
+- **Matplotlib & Seaborn:** Libraries for data visualization, enabling you to plot and analyze data effectively.
 
-- **TensorFlow & PyTorch:** Deep learning frameworks that are widely used in AI research and production environments.  
+- **Scikit-learn:** A comprehensive library for classical machine learning models like linear regression, clustering, etc.
 
-- **Jupyter Notebook:** A fantastic tool for writing and running Python code in an interactive way. Ideal for experimenting with AI models.  
+- **TensorFlow & PyTorch:** Deep learning frameworks that are widely used in AI research and production environments.
+
+- **Jupyter Notebook:** A fantastic tool for writing and running Python code in an interactive way. Ideal for experimenting with AI models.
+
+## 7. Resources
+
+Here are some resources to help you dive deeper into AI development in Python:
 
-## 7. Resources  
-Here are some resources to help you dive deeper into AI development in Python:  
 1. [Python Official Documentation](https://docs.python.org/3/)
 2. [TensorFlow Documentation](https://www.tensorflow.org/guide)
-3. [PyTorch Tutorials Documentation](https://pytorch.org/tutorials/)  
-4. [Scikit-learn Documentation](https://scikit-learn.org/stable/user_guide.html)  
+3. [PyTorch Tutorials Documentation](https://pytorch.org/tutorials/)
+4. [Scikit-learn Documentation](https://scikit-learn.org/stable/user_guide.html)
+
 ---
diff --git a/Amazon Web Services Documentation/aws.md b/Amazon Web Services Documentation/aws.md
index 298905c..3df4609 100644
--- a/Amazon Web Services Documentation/aws.md	
+++ b/Amazon Web Services Documentation/aws.md	
@@ -9,11 +9,8 @@ Amazon Web Services (AWS) is a comprehensive cloud computing platform provided b
 ### Key Concepts of AWS
 
 - Cloud Computing: AWS provides on-demand access to compute, storage, databases, and other resources without having to own physical hardware.
-  
-- Regions and Availability Zones: AWS offers services through geographically distributed **regions, each containing multiple **Availability Zones (AZs). This design ensures high availability, disaster recovery, and redundancy for critical workloads.
-  
+- Regions and Availability Zones: AWS offers services through geographically distributed **regions**, each containing multiple **Availability Zones (AZs)**. This design ensures high availability, disaster recovery, and redundancy for critical workloads.
 - Elasticity & Scalability: AWS enables auto-scaling, allowing services to automatically adjust based on demand.
-  
 - Pay-as-you-go: AWS follows a pay-per-use pricing model, meaning you only pay for the resources you consume.
 
 ---
@@ -23,6 +20,7 @@ Amazon Web Services (AWS) is a comprehensive cloud computing platform provided b
 ### 1. Compute Services
 
 #### EC2 (Elastic Compute Cloud)
+
 - Description: Provides scalable computing capacity in the cloud. EC2 allows users to run virtual servers, called instances, on-demand.
 - Uses:
   - Hosting web applications and APIs.
@@ -31,6 +29,7 @@ Amazon Web Services (AWS) is a comprehensive cloud computing platform provided b
   - Developing and testing software.
 
 #### Lambda
+
 - Description: A serverless compute service where you can run code without provisioning or managing servers.
 - Uses:
   - Running small, independent functions triggered by events.
@@ -38,6 +37,7 @@ Amazon Web Services (AWS) is a comprehensive cloud computing platform provided b
   - Automatically scaling applications.
 
 #### ECS (Elastic Container Service) & EKS (Elastic Kubernetes Service)
+
 - Description: Services for running Docker containers on AWS, with ECS being Amazon’s container orchestration service, and EKS for Kubernetes.
 - Uses:
   - Managing containerized applications.
@@ -47,6 +47,7 @@ Amazon Web Services (AWS) is a comprehensive cloud computing platform provided b
 ### 2. Storage Services
 
 #### S3 (Simple Storage Service)
+
 - Description: Highly scalable object storage with industry-leading availability and durability.
 - Uses:
   - Storing static content like images, videos, and backups.
@@ -55,12 +56,14 @@ Amazon Web Services (AWS) is a comprehensive cloud computing platform provided b
   - Data lakes for big data analytics.
 
 #### EBS (Elastic Block Store)
+
 - Description: Provides block-level storage volumes for use with EC2 instances.
 - Uses:
   - Storing data for databases and enterprise applications.
   - Building scalable application environments that need block storage.
 
 #### Glacier
+
 - Description: Low-cost cloud storage for data archiving and long-term backup.
 - Uses:
   - Archiving infrequently accessed data.
@@ -69,6 +72,7 @@ Amazon Web Services (AWS) is a comprehensive cloud computing platform provided b
 ### 3. Database Services
 
 #### RDS (Relational Database Service)
+
 - Description: Managed relational databases that support MySQL, PostgreSQL, SQL Server, Oracle, and Amazon Aurora.
 - Uses:
   - Hosting production databases.
@@ -76,6 +80,7 @@ Amazon Web Services (AWS) is a comprehensive cloud computing platform provided b
   - High availability and automatic backup and scaling.
 
 #### DynamoDB
+
 - Description: Fully managed NoSQL database that provides low-latency access to data at any scale.
 - Uses:
   - Building high-performance applications such as gaming, retail, and mobile apps.
@@ -83,6 +88,7 @@ Amazon Web Services (AWS) is a comprehensive cloud computing platform provided b
   - Internet of Things (IoT) data storage.
 
 #### Redshift
+
 - Description: Data warehousing service that allows you to run complex queries across petabytes of structured and semi-structured data.
 - Uses:
   - Business intelligence and analytics.
@@ -92,6 +98,7 @@ Amazon Web Services (AWS) is a comprehensive cloud computing platform provided b
 ### 4. Networking Services
 
 #### VPC (Virtual Private Cloud)
+
 - Description: Allows users to create isolated network environments within AWS, offering full control over IP address ranges, subnets, and route tables.
 - Uses:
   - Hosting secure web applications.
@@ -99,6 +106,7 @@ Amazon Web Services (AWS) is a comprehensive cloud computing platform provided b
   - Building VPN connections between on-premises and AWS infrastructure.
 
 #### CloudFront
+
 - Description: AWS’s content delivery network (CDN) service that securely delivers data, videos, applications, and APIs to customers globally.
 - Uses:
   - Accelerating website performance.
@@ -106,6 +114,7 @@ Amazon Web Services (AWS) is a comprehensive cloud computing platform provided b
   - Secure distribution of content.
 
 #### Route 53
+
 - Description: A scalable domain name system (DNS) web service designed to route end users to internet applications.
 - Uses:
   - Managing DNS records for websites.
@@ -115,6 +124,7 @@ Amazon Web Services (AWS) is a comprehensive cloud computing platform provided b
 ### 5. Security, Identity, and Compliance
 
 #### IAM (Identity and Access Management)
+
 - Description: Manage user access and permissions for AWS services.
 - Uses:
   - Fine-grained access control to AWS resources.
@@ -122,6 +132,7 @@ Amazon Web Services (AWS) is a comprehensive cloud computing platform provided b
   - Managing user permissions for large teams.
 
 #### GuardDuty
+
 - Description: A threat detection service that continuously monitors for malicious activity and unauthorized behavior.
 - Uses:
   - Threat detection and response.
@@ -129,14 +140,16 @@ Amazon Web Services (AWS) is a comprehensive cloud computing platform provided b
   - Identifying malicious traffic.
 
 #### WAF (Web Application Firewall)
+
 - Description: Protects web applications from common web exploits that could affect application availability, compromise security, or consume excessive resources.
 - Uses:
   - Mitigating DDoS attacks.
   - Protecting web applications from SQL injection, cross-site scripting (XSS), and other vulnerabilities.
-  
+
 ### 6. Analytics Services
 
 #### Athena
+
 - Description: Interactive query service to analyze data in Amazon S3 using standard SQL.
 - Uses:
   - Analyzing structured or unstructured data stored in S3.
@@ -144,6 +157,7 @@ Amazon Web Services (AWS) is a comprehensive cloud computing platform provided b
   - Querying large datasets without needing ETL processes.
 
 #### Kinesis
+
 - Description: Real-time data streaming service that allows you to collect, process, and analyze data in real-time.
 - Uses:
   - Real-time analytics for IoT data.
@@ -153,6 +167,7 @@ Amazon Web Services (AWS) is a comprehensive cloud computing platform provided b
 ### 7. Machine Learning Services
 
 #### SageMaker
+
 - Description: Fully managed service for building, training, and deploying machine learning models.
 - Uses:
   - Predictive analytics and decision-making applications.
@@ -160,6 +175,7 @@ Amazon Web Services (AWS) is a comprehensive cloud computing platform provided b
   - Building recommendation systems.
 
 #### Rekognition
+
 - Description: Image and video analysis service to identify objects, people, text, and activities.
 - Uses:
   - Facial recognition for security and authentication.
@@ -193,13 +209,13 @@ Amazon Web Services (AWS) is a comprehensive cloud computing platform provided b
 ### Advantages of Using AWS
 
 1. Global Reach: AWS spans over 30 regions and 90 availability zones, ensuring you can deploy applications near your customers for low latency and better user experience.
-  
+
 2. High Scalability: Whether you're a startup or an enterprise, AWS services can scale up or down based on demand, reducing both over-provisioning and under-provisioning.
-  
+
 3. Cost Efficiency: With its pay-as-you-go model, AWS reduces capital expenditure by letting you only pay for the services you use.
 
 4. Security: AWS offers a robust set of security tools, including IAM, GuardDuty, Shield, and WAF, ensuring compliance with the most stringent security and privacy standards.
 
 ---
 
-AWS remains one of the most versatile, scalable, and secure cloud service platforms, allowing businesses of all sizes to innovate and grow efficiently in a competitive market.
\ No newline at end of file
+AWS remains one of the most versatile, scalable, and secure cloud service platforms, allowing businesses of all sizes to innovate and grow efficiently in a competitive market.
diff --git a/Apache Spark Documentation/apache_spark.md b/Apache Spark Documentation/apache_spark.md
index 50f3521..0f6b3cc 100644
--- a/Apache Spark Documentation/apache_spark.md	
+++ b/Apache Spark Documentation/apache_spark.md	
@@ -9,6 +9,7 @@ Apache Spark is an open-source, distributed computing framework designed for hig
 Apache Spark follows a master-slave architecture that includes components like the **Driver Program**, **Cluster Manager**, and **Executors**. Each of these components plays a critical role in distributing and processing data across a cluster of machines.
 
 #### **Driver Program**
+
 - **Role**: The driver is the central control unit for Spark applications. It is responsible for:
   - Launching the user’s program.
   - Maintaining information about the application.
@@ -17,6 +18,7 @@ Apache Spark follows a master-slave architecture that includes components like t
 - **SparkContext**: The entry point to the Spark API, it initializes the application on a cluster and creates an RDD. It establishes the connection between the application and the cluster resources, which it coordinates throughout execution.
 
 #### **Cluster Manager**
+
 - The cluster manager handles resource allocation across the nodes in the cluster. Spark can work with different cluster managers, including:
   - **Standalone**: Spark's native cluster manager for small-scale clusters.
   - **YARN**: Hadoop’s resource manager, suited for environments running Hadoop jobs.
@@ -24,12 +26,14 @@ Apache Spark follows a master-slave architecture that includes components like t
   - **Kubernetes**: A container orchestration system that is growing in popularity for managing Spark on containerized environments.
 
 #### **Executors**
+
 - Executors are the worker nodes in the Spark architecture. Their primary responsibilities include:
   - Executing code sent by the driver.
   - Storing data either in memory or disk (depending on the caching policy).
   - Reporting the results of the computation to the driver.
 
 #### **Jobs, Stages, and Tasks**
+
 - **Job**: A job is created by an action (like `collect()` or `count()`) and triggers the processing of RDDs or DataFrames.
 - **Stages**: Spark breaks a job into stages based on the boundaries defined by wide transformations (like `reduceByKey`).
 - **Tasks**: Each stage consists of multiple tasks that are distributed across the nodes, and these are the basic unit of work executed by Spark.
@@ -41,10 +45,12 @@ Apache Spark follows a master-slave architecture that includes components like t
 Spark is a unified engine that combines various libraries for specific data processing tasks. These include Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX.
 
 #### **Spark Core**
+
 - **Role**: The fundamental engine that provides distributed task scheduling, fault tolerance, memory management, and storage. It handles basic I/O functionalities and is responsible for orchestrating all of Spark’s libraries.
 - **RDD (Resilient Distributed Dataset)**: The core abstraction in Spark, which represents an immutable, distributed collection of objects. RDDs can be created by parallelizing existing collections or loading external datasets (like HDFS, Cassandra, or S3).
 
 #### **Spark SQL**
+
 - **Role**: A module for structured data processing, allowing you to work with data through SQL queries or via a DataFrame API. Spark SQL enables easy integration with databases like Hive, HBase, and Cassandra.
 - **Features**:
   - **DataFrame API**: High-level abstraction that provides a tabular view of data.
@@ -52,21 +58,25 @@ Spark is a unified engine that combines various libraries for specific data proc
   - **Catalyst Optimizer**: Spark SQL's query optimizer that improves the performance of query execution by creating an optimized query plan.
 
 #### **Spark Streaming**
+
 - **Role**: Enables real-time data stream processing. Spark Streaming ingests data in micro-batches (small chunks of data) from sources like Kafka, Flume, and Kinesis, and performs computation using Spark’s API.
 - **DStream (Discretized Stream)**: The abstraction used in Spark Streaming for continuous data streams. DStreams are built on top of RDDs, ensuring fault tolerance.
 - **Use Cases**: Real-time analytics, monitoring, and event detection (e.g., fraud detection in financial transactions, log processing).
 
 #### **MLlib (Machine Learning Library)**
+
 - **Role**: A distributed machine learning library that provides scalable algorithms for classification, regression, clustering, and collaborative filtering. It also includes tools for feature extraction, transformation, and pipeline creation.
 - **Algorithms Supported**: Includes linear models, decision trees, random forests, gradient boosting, K-means clustering, and more.
 - **Pipelines**: Simplifies machine learning workflows by providing an API to combine multiple stages of learning (like preprocessing, model fitting, and evaluation).
 
 #### **GraphX**
+
 - **Role**: A library for processing large-scale graphs and graph-parallel computation. It provides an optimized runtime for building and transforming graphs, performing graph analytics, and working with graph-based machine learning.
 - **Graph Representation**: GraphX uses a combination of RDDs to represent vertices and edges, which can be transformed using operations like `mapVertices` and `aggregateMessages`.
 - **Use Cases**: GraphX is ideal for applications like social network analysis, recommendation systems, and pathfinding.
 
 #### **SparkR**
+
 - **Role**: Provides an interface to use Spark within R programs. SparkR enables data scientists to leverage Spark’s distributed processing power without leaving their familiar R environment.
 - **Use Cases**: Used in scenarios where R users want to apply distributed machine learning algorithms or process large datasets without switching languages.
 
@@ -74,9 +84,10 @@ Spark is a unified engine that combines various libraries for specific data proc
 
 ### **3. Working with Apache Spark**
 
-Spark’s primary abstraction for distributed data processing is the **RDD** (Resilient Distributed Dataset). It allows operations on large datasets across a cluster of computers. 
+Spark’s primary abstraction for distributed data processing is the **RDD** (Resilient Distributed Dataset). It allows operations on large datasets across a cluster of computers.
 
 #### **RDD Operations**
+
 RDDs support two types of operations: **Transformations** and **Actions**.
 
 - **Transformations**: These are lazy operations that define a new RDD from an existing one. Spark doesn’t execute these operations immediately but records the lineage. Only when an action is called, Spark runs the actual computation.
@@ -84,7 +95,6 @@ RDDs support two types of operations: **Transformations** and **Actions**.
     - `map()`: Applies a function to each element of the RDD.
     - `filter()`: Filters elements of the RDD based on a predicate function.
     - `groupByKey()`: Groups values for each key in the RDD.
-  
 - **Actions**: Actions cause Spark to execute the transformations and return results. These trigger the computation of the entire RDD transformation pipeline.
   - Examples of actions include:
     - `collect()`: Collects all elements of the RDD and brings them to the driver.
@@ -92,6 +102,7 @@ RDDs support two types of operations: **Transformations** and **Actions**.
     - `saveAsTextFile()`: Saves the RDD’s content to an external storage like HDFS.
 
 #### **DataFrames and Datasets**
+
 - **DataFrames**: Similar to RDDs but with named columns, DataFrames provide an easier-to-use, higher-level API. DataFrames can be constructed from various data sources such as structured files, tables in Hive, or external databases.
 - **Datasets**: A Dataset is a typed version of a DataFrame, combining the performance benefits of DataFrames with the benefits of type-safety in traditional object-oriented programming.
 
@@ -102,9 +113,11 @@ RDDs support two types of operations: **Transformations** and **Actions**.
 Spark ensures fault tolerance through the lineage of RDDs. The system can recover lost data automatically by recomputing the transformations from the original dataset.
 
 #### **RDD Lineage**
+
 - Every RDD maintains a record of how it was derived from other RDDs. If a node crashes and its RDD partitions are lost, Spark will rebuild the lost partitions from the parent RDDs.
 
 #### **Checkpoints**
+
 - For long RDD chains, Spark supports **checkpointing**, which saves intermediate results to stable storage (like HDFS). This can break the lineage and speed up recovery for very large datasets.
 
 ---
@@ -144,6 +157,7 @@ word_count.saveAsTextFile("hdfs://path/to/output")
 ```
 
 In this example:
+
 - **flatMap()** splits each line into words.
 - **map()** converts each word into a pair `(word, 1)`.
 - **reduceByKey()** aggregates the count of each word.
@@ -152,9 +166,7 @@ In this example:
 
 ### **7. Performance Optimization Techniques**
 
-While Apache Spark is fast, certain optim
-
-izations can further improve performance:
+While Apache Spark is fast, certain optimizations can further improve performance:
 
 - **Caching**: Frequently used RDDs or DataFrames can be cached in memory using `cache()` or `persist()`, reducing computation time for repeated actions.
 - **Partitioning**: Ensure proper partitioning of data, especially when using operations like joins. Custom partitioning can improve performance by minimizing data shuffles.
@@ -166,11 +178,13 @@ izations can further improve performance:
 ### **8. When to Use and When Not to Use Apache Spark**
 
 #### **When to Use Spark**
+
 - **Large-scale Data Processing**: Spark is ideal when the dataset is too large to be processed on a single machine.
 - **Iterative Machine Learning**: Repeated computations over the same dataset benefit from Spark’s in-memory processing.
 - **Real-time Processing**: Spark Streaming is suitable for applications that need near real-time processing of data.
 
 #### **When Not to Use Spark**
+
 - **Small Data**: For small datasets, tools like Pandas or Dask may offer simpler solutions with less overhead.
 - **Complex Transactional Workloads**: Spark isn’t optimized for complex, transactional database queries like those handled by traditional RDBMS.
 - **High-Latency Tolerance**: While Spark is fast, for ultra-low-latency applications (e.g., trading systems), specialized streaming systems like Apache Flink may perform better.
@@ -179,4 +193,4 @@ izations can further improve performance:
 
 ### **Conclusion**
 
-Apache Spark is a powerful framework that brings speed, scalability, and ease of use to big data processing. It supports a wide range of workloads, from ETL to machine learning and real-time streaming, making it a versatile tool for modern data engineering and data science applications. However, like any tool, it should be applied judiciously based on the scale and complexity of the task at hand.
\ No newline at end of file
+Apache Spark is a powerful framework that brings speed, scalability, and ease of use to big data processing. It supports a wide range of workloads, from ETL to machine learning and real-time streaming, making it a versatile tool for modern data engineering and data science applications. However, like any tool, it should be applied judiciously based on the scale and complexity of the task at hand.
diff --git a/Architecture Systems/README.md b/Architecture Systems/README.md
index fdf7f86..e802e4b 100644
--- a/Architecture Systems/README.md	
+++ b/Architecture Systems/README.md	
@@ -15,14 +15,17 @@ Computer system architecture determines how hardware and software interact, infl
 A typical computer system consists of several key components that work together to perform complex tasks:
 
 ### 2.1 Central Processing Unit (CPU)
+
 The CPU is the brain of the computer, responsible for executing instructions provided by software. It consists of two main components:
+
 - **Control Unit (CU):** Manages the execution of instructions by directing the various parts of the CPU to perform specific tasks.
 - **Arithmetic Logic Unit (ALU):** Handles mathematical operations like addition, subtraction, and logical operations like AND, OR, and NOT.
 
 The CPU is connected to other parts of the system via a series of buses that enable communication.
 
 ### 2.2 Memory
-Memory is divided into primary and secondary storage. 
+
+Memory is divided into primary and secondary storage.
 
 - **Primary Storage:** Refers to volatile memory such as RAM (Random Access Memory), which temporarily holds data that the CPU uses during operation.
 - **Secondary Storage:** Includes non-volatile storage devices like hard drives (HDDs) and solid-state drives (SSDs), which store data long-term.
@@ -30,14 +33,18 @@ Memory is divided into primary and secondary storage.
 Memory is crucial in determining the overall speed and performance of the system since the CPU often needs to access it to retrieve data.
 
 ### 2.3 Input/Output (I/O) Devices
+
 I/O devices allow the system to communicate with the external environment. These include:
+
 - **Input devices:** Keyboards, mice, scanners, etc.
 - **Output devices:** Monitors, printers, etc.
 
 These devices are managed through controllers that translate I/O signals into a form the CPU and memory can use.
 
 ### 2.4 Buses and Communication
+
 A **bus** is a communication system that transfers data between components inside or outside a computer. It consists of three main types:
+
 1. **Data Bus:** Carries the actual data.
 2. **Address Bus:** Carries memory addresses where data should be read or written.
 3. **Control Bus:** Carries control signals, such as memory read/write commands.
@@ -47,21 +54,27 @@ A **bus** is a communication system that transfers data between components insid
 ## 3. Types of Computer System Architecture
 
 ### 3.1 Von Neumann Architecture
+
 Named after John von Neumann, this is the most widely used architecture in modern computers. In this design:
+
 - A single memory is used for both data and instructions.
 - Instructions are fetched from memory and executed sequentially.
 
 This architecture is simple but suffers from the **von Neumann bottleneck**, where the CPU waits on slow memory to fetch instructions.
 
 ### 3.2 Harvard Architecture
+
 In contrast to Von Neumann, the **Harvard architecture** has separate memory storage for instructions and data. This separation allows instructions and data to be accessed simultaneously, improving performance.
 
 ### 3.3 Instruction Set Architecture (ISA)
+
 ISA defines the set of instructions that a computer’s CPU can execute. It serves as the boundary between hardware and software. Examples include:
+
 - **x86:** A common ISA used in most personal computers.
 - **ARM:** An energy-efficient ISA commonly used in mobile devices.
 
 ### 3.4 Microarchitecture
+
 While the ISA defines what the CPU can do, the **microarchitecture** defines how it is done. This involves the physical design of the CPU's circuits, execution units, cache, and pipelines.
 
 ---
@@ -69,18 +82,23 @@ While the ISA defines what the CPU can do, the **microarchitecture** defines how
 ## 4. CPU Design and Architecture
 
 ### 4.1 Instruction Cycle
+
 The instruction cycle is the basic operational process of the CPU. It consists of three main stages:
+
 1. **Fetch:** The CPU retrieves an instruction from memory.
 2. **Decode:** The instruction is decoded to understand what action needs to be performed.
 3. **Execute:** The CPU performs the required action, such as performing a calculation or moving data.
 
 ### 4.2 Pipelining
+
 **Pipelining** allows the CPU to process multiple instructions at different stages of the instruction cycle simultaneously. This improves throughput by ensuring the CPU is always working on an instruction at each clock cycle.
 
 ### 4.3 Superscalar Architecture
+
 Superscalar architecture enhances pipelining by allowing multiple instructions to be fetched, decoded, and executed simultaneously in different execution units.
 
 ### 4.4 Multicore Processors
+
 A **multicore processor** contains multiple CPU cores on a single chip. Each core can execute instructions independently, allowing for parallel execution of tasks, thus improving performance in multi-threaded applications.
 
 ---
@@ -88,17 +106,22 @@ A **multicore processor** contains multiple CPU cores on a single chip. Each cor
 ## 5. Memory Hierarchy and Architecture
 
 ### 5.1 Registers, Cache, RAM, and Secondary Storage
+
 The memory hierarchy ensures a balance between speed and cost:
+
 1. **Registers:** The fastest, smallest memory located inside the CPU.
 2. **Cache:** Faster than RAM but smaller, cache stores frequently accessed data.
 3. **RAM:** The main memory where programs are loaded for execution.
 4. **Secondary Storage:** Slower but larger storage for permanent data, such as hard drives.
 
 ### 5.2 Virtual Memory
+
 **Virtual memory** allows the system to compensate for a lack of physical RAM by using a portion of the hard drive as if it were RAM. This technique enables systems to run larger applications or more applications simultaneously than physical memory alone would allow.
 
 ### 5.3 Memory Access Methods
+
 Different methods are used to access memory, such as:
+
 - **Random Access Memory (RAM):** Any byte of memory can be accessed directly without touching the preceding bytes.
 - **Sequential Access Memory (SAM):** Data must be accessed in a specific sequence (e.g., tape storage).
 
@@ -107,15 +130,19 @@ Different methods are used to access memory, such as:
 ## 6. Parallel Processing and Multiprocessing
 
 ### 6.1 Single Instruction, Multiple Data (SIMD)
+
 SIMD allows a single instruction to operate on multiple data points simultaneously. It is commonly used in graphics processing and scientific computations.
 
 ### 6.2 Multiple Instruction, Multiple Data (MIMD)
+
 In MIMD systems, different processors execute different instructions on different pieces of data simultaneously. This is used in more complex systems like distributed computing.
 
 ### 6.3 Symmetric Multiprocessing (SMP)
+
 In SMP systems, multiple processors share the same memory and are managed by a single operating system. SMP is commonly used in server environments for high-performance computing.
 
 ### 6.4 Distributed Systems
+
 In distributed systems, multiple computers are networked together to share resources and work on tasks collaboratively. These systems offer scalability and fault tolerance.
 
 ---
@@ -123,12 +150,15 @@ In distributed systems, multiple computers are networked together to share resou
 ## 7. Input/Output Architecture
 
 ### 7.1 I/O Ports and Interfaces
+
 I/O ports and interfaces serve as the communication gateways between the CPU and external devices. Examples include USB, PCIe, and SATA.
 
 ### 7.2 Direct Memory Access (DMA)
+
 DMA allows peripherals to directly transfer data to or from memory without involving the CPU. This improves system performance by freeing up the CPU for other tasks.
 
 ### 7.3 Interrupts and Polling
+
 **Interrupts** allow a device to signal the CPU that it requires attention, allowing efficient I/O operations. **Polling**, on the other hand, involves the CPU regularly checking the status of an I/O device, which can be less efficient.
 
 ---
@@ -136,15 +166,19 @@ DMA allows peripherals to directly transfer data to or from memory without invol
 ## 8. Modern Advancements in Architecture
 
 ### 8.1 Reduced Instruction Set Computing (RISC)
+
 RISC architecture uses a small, highly optimized set of instructions, allowing for fast execution. It is used in mobile processors like ARM.
 
 ### 8.2 Complex Instruction Set Computing (CISC)
+
 CISC architecture uses a larger, more complex set of instructions. This allows for more efficient use of memory but can slow down processing speed compared to RISC.
 
 ### 8.3 Graphics Processing Units (GPUs) in Modern Systems
+
 GPUs are specialized processors designed for parallel processing. They are used not only for rendering graphics but also for tasks such as machine learning and scientific simulations.
 
 ### 8.4 Quantum Computing
+
 Quantum computers use quantum bits (qubits) to perform calculations that would be infeasible for classical computers. While still in the experimental stage, quantum computing promises to revolutionize fields such as cryptography and material science.
 
 ---
@@ -154,4 +188,3 @@ Quantum computers use quantum bits (qubits) to perform calculations that would b
 In this document, we explored the fundamental aspects of computer system architecture, covering topics from basic CPU design to modern advancements like quantum computing. Understanding the architecture of computer systems is essential for optimizing performance, developing software, and advancing hardware technologies.
 
 As computing continues to evolve, the importance of understanding system architecture will only grow, especially as new paradigms like quantum computing emerge.
-
diff --git a/Arduino Nano Documentation/arduino_nano.md b/Arduino Nano Documentation/arduino_nano.md
index 8f44e6d..7965f1f 100644
--- a/Arduino Nano Documentation/arduino_nano.md	
+++ b/Arduino Nano Documentation/arduino_nano.md	
@@ -45,7 +45,7 @@ There are 8 analog input pins (A0-A7). These pins read an input voltage between
 ##### **Communication Pins**
 - **RX (D0)**: Receive data for serial communication.
 - **TX (D1)**: Transmit data for serial communication.
-- **SPI (D10, D11, D12, D13)**: Used for SPI communication. 
+- **SPI (D10, D11, D12, D13)**: Used for SPI communication.
 - **I2C (A4 for SDA, A5 for SCL)**: Used for I2C communication.
 - **Reset Pin**: Shorting this pin to GND will reset the microcontroller.