What is the purpose of data cleaning in analysis?

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Supervised and Unsupervised Learning are two primary types of machine learning, differing mainly in how they process and learn from data.

Neural networks are a type of machine learning model inspired by the structure and function of the human brain. They are designed to recognize patterns and relationships in data through a process of learning.

The purpose of data cleaning in analysis is to ensure the data is accurate, consistent, and reliable before any meaningful insights or decisions are made. It involves identifying and correcting (or removing) errors, inconsistencies, and inaccuracies in the dataset.

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Why Data Cleaning Is Important:

1. Improves Data Quality:
   Removes duplicates, fixes typos, handles missing values, and resolves inconsistencies to create a trustworthy dataset.

2. Ensures Accurate Analysis:
   Dirty or flawed data can lead to misleading results, incorrect conclusions, and poor decision-making.

3. Enhances Model Performance:
   In machine learning or statistical modeling, clean data leads to better training and more accurate predictions.

4. Saves Time and Resources:
   Catching errors early reduces the need for rework and prevents costly mistakes down the line.

5. Supports Compliance:
   Helps meet data governance and regulatory requirements by maintaining data integrity.

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Common Data Cleaning Tasks:

• Handling missing or null values

• Removing duplicates

• Correcting formatting errors

• Standardizing data (e.g., date formats, units)

• Filtering outliers or irrelevant data

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