✅ 5 of the best Kaggle datasets
💸 For data science projects (in finance)

👨🏻‍💻 If you are looking for datasets to do financial projects, the datasets presented on the Kaggle site can be a great option.

⏪ These datasets are usually clean and ready to use and are very suitable for machine learning models. Some of these datasets are even updated daily and you can use them for deeper analysis.👇


1️⃣ S&P 500 stock dataset (daily update)

📎 Link: S&P 500 Stocks


2️⃣ Database of loans and debts

📎 Link: Loans & Liability


3️⃣ Dataset of frequent use of credit card

📎 Link: Credit Card Spending Habits


4️⃣ Company bankruptcy prediction dataset

📎 Link: Company Bankruptcy Prediction


5️⃣ Credit score classification dataset

📎 Link: Credit score classification

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11 types of variables in a Dataset

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Datasets Guide 📚

A practical and beginner-friendly guide that walks you through everything you need to know about datasets in machine learning and deep learning. This guide explains how to load, preprocess, and use datasets effectively for training models. It's an essential resource for anyone working with LLMs or custom training workflows, especially with tools like Unsloth.

Importance:
Understanding how to properly handle datasets is a critical step in building accurate and efficient AI models. This guide simplifies the process, helping you avoid common pitfalls and optimize your data pipeline for better performance.

Link: https://docs.unsloth.ai/basics/datasets-guide

#MachineLearning #DeepLearning #Datasets #DataScience #AI #Unsloth #LLM #TrainingData #MLGuide

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Topic: Handling Datasets of All Types – Part 1 of 5: Introduction and Basic Concepts

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1. What is a Dataset?

• A dataset is a structured collection of data, usually organized in rows and columns, used for analysis or training machine learning models.

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2. Types of Datasets

• Structured Data: Tables, spreadsheets with rows and columns (e.g., CSV, Excel).

• Unstructured Data: Images, text, audio, video.

• Semi-structured Data: JSON, XML files containing hierarchical data.

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3. Common Dataset Formats

• CSV (Comma-Separated Values)

• Excel (.xls, .xlsx)

• JSON (JavaScript Object Notation)

• XML (eXtensible Markup Language)

• Images (JPEG, PNG, TIFF)

• Audio (WAV, MP3)

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4. Loading Datasets in Python

• Use libraries like pandas for structured data:

import pandas as pd
df = pd.read_csv('data.csv')

• Use libraries like json for JSON files:

import json
with open('data.json') as f:
    data = json.load(f)

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5. Basic Dataset Exploration

• Check shape and size:

print(df.shape)

• Preview data:

print(df.head())

• Check for missing values:

print(df.isnull().sum())

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6. Summary

• Understanding dataset types is crucial before processing.

• Loading and exploring datasets helps identify cleaning and preprocessing needs.

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Exercise

• Load a CSV and JSON dataset in Python, print their shapes, and identify missing values.

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The rest of the parts 👇
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