📊 Data Collection – Machine Learning Foundations 🧠

This folder focuses on Data Collection, which is the first and most critical step in any Machine Learning pipeline. The quality of data directly impacts model accuracy and performance.

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🔍 What is Data Collection?

Data Collection is the process of gathering raw data from different sources that can be used for:

• Analysis
• Visualization
• Machine Learning model training

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🗂️ Data Sources Used

• 📁 CSV Files
• 📊 Excel Sheets
• 🌐 Online datasets (e.g., Kaggle, open data sources)
• 🧪 Sample / synthetic datasets for practice

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🛠️ Tools & Libraries Used

• Python 🐍
• Pandas
• NumPy
• Jupyter Notebook

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📌 Concepts Covered

• Reading data using pandas.read_csv()
• Loading Excel files
• Understanding dataset structure
• Checking rows, columns & data types
• Handling missing values (basic level)
• Initial data inspection

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📂 Folder Structure

Data_Collection/
│
├── data_collection.ipynb
├── dataset.csv
└── README.md

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🎯 Learning Goals

• Understand how real-world data is collected

• Learn to load datasets efficiently

• Build a strong foundation for:

  • Data Cleaning
  • Visualization
  • Machine Learning

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🚀 Next Step

➡️ Data Cleaning & Data Visualization

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⭐ Good data beats complex algorithms. Keep collecting, exploring, and learning! 💪📈