📊 Data Science in Python 🚀

shoaib1522/Data-Science-In-Python

A comprehensive collection of data science projects, experiments, and tools, showcasing my journey and expertise in Python, data analysis, and machine learning. This repository is a one-stop resource for data-driven enthusiasts looking to dive into Python for data science. 🌟

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🌐 Overview

This repository contains a series of projects and assignments developed during my data science learning journey. From understanding the basics of Python to advanced concepts like exploratory data analysis (EDA), machine learning, and data visualization, this repository highlights:

• Real-world datasets used for analysis and prediction.
• Custom Python scripts for data aggregation and processing.
• Hands-on experience with popular libraries like Pandas, NumPy, Matplotlib, and Scikit-learn.

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📁 Directory Structure

Here’s a quick glance at the repository organization:

📂 Data-Science-In-Python/
├── GIT/  
│   └── Git commands, logs, and hands-on exercises.  
├── Data Aggregator Tool/  
│   ├── Data-Aggregator-Tool-In-Python/  
│   └── Scripts for user data processing and immutable data management.  
├── Python Programming/  
│   └── Fundamental Python concepts and third assignment solutions.  
├── Climate Research Analysis/  
│   └── Climate data analysis and insights using Python.  
├── Pandas Data Frames/  
│   └── Data analysis on sales, customers, and balance datasets.  
├── Data-Sets Working/  
│   └── Hands-on practice with diverse datasets (e.g., Toyota, NVIDIA stock, London weather).  
├── IDS-Project-EDA/  
│   └── Credit card fraud detection and other EDA-focused tasks.  
├── Labs (1-5)/  
│   └── Weekly labs focusing on EDA, Kaggle projects, and Pandas.  
├── LICENSE  
├── README.md  
└── .gitignore  

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⚡ Highlights

🔎 Exploratory Data Analysis (EDA)

• Datasets: Marketing data, weather analysis, and stock performance (e.g., NVIDIA, Toyota).
• Tools: Pandas, Matplotlib, Seaborn for detailed insights and visualizations.

🛠️ Data Aggregation & Transformation

• Scripts: Python-based tools for processing user data and managing dictionaries.

🐍 Python Programming

• Concepts: Data structures, functions, loops, and error handling.
• Applications: Assignments demonstrating mastery of Python fundamentals.

🌍 Climate Research Analysis

• Insights on global weather patterns using Jupyter notebooks.

🧠 Machine Learning (ML)

• Use Case: Fraud detection using credit card transaction data.
• Libraries: Scikit-learn, NumPy, and Pandas for preprocessing and modeling.

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📚 Technologies & Libraries

Core Python Libraries

• Pandas: Data manipulation and analysis.
• NumPy: Numerical computing.
• Matplotlib & Seaborn: Data visualization.
• Scikit-learn: Machine learning algorithms.
• Jupyter Notebook: Interactive coding environment.

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🤝 Contributors

• Shoaib Ahmad (GitHub Profile)
  Dedicated to exploring and mastering data science with Python.

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📸 Screenshots

Sample Visualizations and Logs

• EDA Examples: Toyota stock trends, weather patterns.
• Git Logs: Demonstrating version control mastery.

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🚀 How to Run

1. Clone the repository:

   git clone https://github.com/shoaib1522/Data-Science-In-Python.git

2. Navigate to the desired project folder.
3. Open .ipynb files with Jupyter Notebook or VSCode for execution.

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📜 License

This repository is licensed under the MIT License. Feel free to explore, learn, and contribute!

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