In order to access the building, one must pass through a gate. To gain entry to the car park, the barrier needs to be opened. To reach a specific floor, a 'tablet' must be utilized. Every entry is recorded, noting the individuals, the dates, and the times of their arrival.

Is it possible to analyze the available data and ascertain the arrival methods of each office visitor, allowing for predictions? For instance, "User 5 arrives at gate 4 every Monday at 8 am," or "User 18 visits at 11 am on Saturdays, except for the last day of the month." Are there patterns within the current data?

This is a synthetic learning dataset for linear models' practice.

The file contain dataset with two variables (x & y). The dataset is for Linear regression ML Models. The dataset can be used for Testing purpose. The x variable is the independent variable, and y is the dependent variable. The dataset has a correlation of 0.9981 showing the dataset is best suited for linear models and can be used for the testing purpose.

Dataset

This dataset was created by Oleksiy Kononenko

Released under Apache 2.0

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Dataset

This dataset was created by Abhishek Kumar

Released under Apache 2.0

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Dataset

This dataset was created by Esraa_Fouad777

Released under Apache 2.0

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The aim of this data set is to be used along with my notebook Linear Regression Notes which provides a guideline for applying correlation analysis and linear regression models from a statistical approach.

A fictional call center is interested in knowing the relationship between the number of personnel and some variables that measure their performance such as average answer time, average calls per hour, and average time per call. Data were simulated to represent 200 shifts.

Dataset

This dataset was created by EESHAKHANZADI

Released under Apache 2.0

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Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Dataset

This dataset was created by Yerzat Tursunkulov

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Dataset

This dataset was created by Suhail Sajid

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This dataset represents a synthetic relationship between weight and height, designed to exhibit a non-linear, polynomial trend suitable for polynomial regression analysis. The dataset includes 50 data points where "Weight" is the independent variable and "Height" is the dependent variable. Unlike a simple linear trend, this dataset's pattern follows a curved path, making it ideal for demonstrating polynomial regression models and machine learning techniques that address non-linear relationships. Suggested Applications: Polynomial regression modeling Non-linear data visualization Machine learning algorithm experimentation

This dataset contains a collection of 100 randomly generated data points representing the relationship between the number of hours a student spends studying and their corresponding performance, measured as a score. The data has been generated to simulate a real-world scenario where study hours are assumed to influence academic outcomes, making it an excellent resource for linear regression analysis and other machine learning tasks.

Each row in the dataset consists of:

Hours: The number of hours a student dedicates to studying, ranging between 0 and 10 hours. Scores: The student's performance score, represented as a percentage, ranging from 0 to 100. Use Cases: This dataset is particularly useful for:

Linear Regression: Exploring how study hours influence student performance, fitting a regression line to predict scores based on study time. Data Science & Machine Learning: Practicing regression analysis, training models, and applying other predictive algorithms. Educational Research: Simulating data-driven insights into student behavior and performance metrics. Features: 100 rows of data. Continuous numerical variables suitable for regression tasks. Generated for educational purposes, making it ideal for students, teachers, and beginners in machine learning and data science. Potential Applications: Build a linear regression model to predict student scores. Investigate the correlation between study time and performance. Apply data visualization techniques to better understand the data. Use the dataset to experiment with model evaluation metrics like Mean Squared Error (MSE) and R-squared.

This dataset has been created to demonstrate the use of a simple linear regression model. It includes two variables: an independent variable and a dependent variable. The data can be used for training, testing, and validating a simple linear regression model, making it ideal for educational purposes, tutorials, and basic predictive analysis projects. The dataset consists of 100 observations with no missing values, and it follows a linear relationship

Description: This dataset is designed for predicting energy consumption based on various building features and environmental factors. It contains data for multiple building types, square footage, the number of occupants, appliances used, average temperature, and the day of the week. The goal is to build a predictive model to estimate energy consumption using these attributes.

The dataset can be used for training machine learning models such as linear regression to forecast energy needs based on the building's characteristics. This is useful for understanding energy demand patterns and optimizing energy consumption in different building types and environmental conditions.

Based on the file snippets, your dataset, "Advertising.csv," is a classic dataset used for marketing and data analysis. It explores the relationship between advertising budgets and product sales.

Here is a breakdown of the columns:

• TV: This column represents the advertising budget (likely in thousands of dollars) spent on television commercials.
• Radio: This column shows the advertising budget allocated to radio broadcasts.
• Newspaper: This column contains the advertising budget for newspaper ads.
• Sales: This is your target variable. It represents the sales of a product (likely in thousands of units) for a given advertising budget.

In essence, this dataset is designed to help you answer questions like:

• Which advertising channel (TV, Radio, or Newspaper) is the most effective at driving sales?
• How does spending on each channel affect overall sales?
• Can we predict future sales based on a planned advertising budget?

This dataset is frequently used to practice and demonstrate concepts in linear regression and marketing mix modeling.

Home Value Insights: A Beginner's Regression Dataset

This dataset is designed for beginners to practice regression problems, particularly in the context of predicting house prices. It contains 1000 rows, with each row representing a house and various attributes that influence its price. The dataset is well-suited for learning basic to intermediate-level regression modeling techniques.

Features:

1. Square_Footage: The size of the house in square feet. Larger homes typically have higher prices.
2. Num_Bedrooms: The number of bedrooms in the house. More bedrooms generally increase the value of a home.
3. Num_Bathrooms: The number of bathrooms in the house. Houses with more bathrooms are typically priced higher.
4. Year_Built: The year the house was built. Older houses may be priced lower due to wear and tear.
5. Lot_Size: The size of the lot the house is built on, measured in acres. Larger lots tend to add value to a property.
6. Garage_Size: The number of cars that can fit in the garage. Houses with larger garages are usually more expensive.
7. Neighborhood_Quality: A rating of the neighborhood’s quality on a scale of 1-10, where 10 indicates a high-quality neighborhood. Better neighborhoods usually command higher prices.
8. House_Price (Target Variable): The price of the house, which is the dependent variable you aim to predict.

Potential Uses:

1. Beginner Regression Projects: This dataset can be used to practice building regression models such as Linear Regression, Decision Trees, or Random Forests. The target variable (house price) is continuous, making this an ideal problem for supervised learning techniques.

2. Feature Engineering Practice: Learners can create new features by combining existing ones, such as the price per square foot or age of the house, providing an opportunity to experiment with feature transformations.

3. Exploratory Data Analysis (EDA): You can explore how different features (e.g., square footage, number of bedrooms) correlate with the target variable, making it a great dataset for learning about data visualization and summary statistics.

4. Model Evaluation: The dataset allows for various model evaluation techniques such as cross-validation, R-squared, and Mean Absolute Error (MAE). These metrics can be used to compare the effectiveness of different models.

Versatility:

• The dataset is highly versatile for a range of machine learning tasks. You can apply simple linear models to predict house prices based on one or two features, or use more complex models like Random Forest or Gradient Boosting Machines to understand interactions between variables.

• It can also be used for dimensionality reduction techniques like PCA or to practice handling categorical variables (e.g., neighborhood quality) through encoding techniques like one-hot encoding.

• This dataset is ideal for anyone wanting to gain practical experience in building regression models while working with real-world features.

Dataset

This dataset was created by Shubham Singh

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Dataset

This dataset was created by RakeshSk

Contents

I simply motivated by the competition posted by Kaggle. This Dataset has beautiful visualization created with the help of matplotlib and Seaborn. I also used the pandas and Numpy

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?