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https://github.com/imnotamr/datasets-used

A comprehensive collection of datasets for machine learning and data science projects, covering topics from advertising and sales to health and sports analytics
https://github.com/imnotamr/datasets-used

ai classification data-analysis data-science data-visualization deep-learning jupyter-notebook machine-learning models python regression-models

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A comprehensive collection of datasets for machine learning and data science projects, covering topics from advertising and sales to health and sports analytics

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README 

        
# 📊 Datasets for Machine Learning Projects

This directory contains various datasets used for machine learning, data analysis, and visualization tasks. Each dataset serves a specific purpose and covers different domains, providing a range of options for exploratory data analysis, regression, classification, and more.



## "I haven't uploaded all of the datasets I've used yet :)" ## 



# 📁 Dataset Descriptions

1)	Advertising.csv

•	Description: Contains data on advertising expenditures across different channels (TV, Radio, Newspaper) and their impact on sales.

•	Usage: Great for regression analysis and predictive modeling to analyze the impact of advertising on sales.



2)	SVM_Data.csv

•	Description: A dataset designed for Support Vector Machine (SVM) classification tasks.

•	Usage: Suitable for experimenting with SVM algorithms, tuning hyperparameters, and evaluating model accuracy.



4)	Salary.csv

•	Description: Contains information on individuals' salary levels based on factors like education, experience, and position.

•	Usage: Ideal for regression modeling, predicting salary levels based on features, and understanding salary trends.



6)	adult.csv

•	Description: UCI Adult dataset, used to predict income levels based on demographic features.

•	Usage: Commonly used for classification tasks, especially binary classification on predicting income levels (e.g., above or below 50K).



8)	car_data.csv

•	Description: Information on various car attributes and evaluations.

•	Usage: Useful for classification tasks, such as evaluating car acceptability based on features like buying price, maintenance cost, and safety.



10)	insurance.csv

•	Description: Insurance data, including demographic and medical history factors, along with insurance charges.

•	Usage: Great for regression analysis, predicting insurance costs, and understanding factors influencing charges.



12)	kc_house_data.csv

•	Description: Contains house prices and associated features from the King County housing dataset.

•	Usage: Suitable for regression modeling, predicting house prices, and feature importance analysis.



14)	titanic.csv

•	Description: Famous Titanic dataset containing details about passengers and their survival status.

•	Usage: Ideal for binary classification, survival analysis, and feature engineering experiments.



16)	IBM Dataset.csv

•	Description: IBM HR Analytics dataset, used to predict employee attrition and understand HR factors.

•	Usage: Useful for classification tasks, especially for predicting employee turnover.



18)	Iris.csv

•	Description: Classic Iris dataset containing measurements of different iris flower species.

•	Usage: Commonly used for classification tasks and exploring clustering algorithms.



20)	SampleSuperstore.csv

•	Description: Data from a sample superstore, including sales, profits, and product categories.

•	Usage: Useful for analyzing sales performance, regional trends, and market segmentation.



22)	data.csv

•	Description: General dataset for testing and exploratory analysis.

•	Usage: Used for various data analysis tasks.



24)	diabetes.csv

•	Description: Diabetes dataset containing health-related features used to predict diabetes onset.

•	Usage: Suitable for binary classification and medical analysis tasks.



26)	fifa.csv

•	Description: FIFA dataset with player statistics.

•	Usage: Ideal for data exploration, player comparison, and sports analytics.



28)	pendigits-test.csv & pendigits-train.csv

•	Description: Handwritten digit recognition dataset with training and testing splits.

•	Usage: Commonly used for classification tasks and testing digit recognition algorithms.



30)	regression_project_data.csv

•	Description: Dataset for regression analysis.

•	Usage: Used for regression modeling and feature analysis.



32)	tips.csv

•	Description: Dataset containing restaurant tips, including features like total bill, tip amount, and day.

•	Usage: Great for exploring regression, correlation, and understanding factors affecting tips.



34)	titanic.csv (duplicate)

•	Description: Duplicate of the Titanic dataset, used for different experimental setups.

•	Usage: Same as above, for binary classification and survival analysis.



# 📚 How to Use

Load the Data

Each dataset is in CSV format and can be loaded using pandas in Python:



python

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import pandas as pd



data = pd.read_csv('path/to/dataset.csv')

Exploratory Data Analysis

Perform initial analysis to understand the features, data types, and any necessary preprocessing steps:



python

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data.info()

data.describe()

Machine Learning

These datasets are suitable for a variety of machine learning tasks such as regression, classification, clustering, and feature engineering.



# 💡 Suggested Applications

Regression Models: Advertising.csv, insurance.csv, kc_house_data.csv

Classification Models: adult.csv, car_data.csv, titanic.csv, Iris.csv, diabetes.csv

Exploratory Data Analysis: SampleSuperstore.csv, fifa.csv

Time-Series & Sequential Data: pendigits-train.csv and pendigits-test.csv

# 📄 License

These datasets are publicly available for educational and research purposes. Please ensure proper citation when using these datasets for publications or sharing results.