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Explore and analyze Iris dataset with SPSS for comprehensive classification insights. Implement statistical methods for robust data classification and gain valuable patterns from this well-known dataset

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Iris Data Classification Analysis using SPSS

This repository contains code and resources for performing classification analysis on the Iris dataset using IBM SPSS Statistics software.

Overview

The Iris dataset is a famous dataset in the field of machine learning and statistics. It contains measurements of various iris flowers, categorized into three species: setosa, versicolor, and virginica. In this project, we utilize SPSS to analyze this dataset and build classification models to predict the species of iris flowers based on their measurements.

Files

  • iris_data.csv: CSV file containing the Iris dataset.
  • iris_analysis_syntax.sps: SPSS syntax file for data analysis.
  • iris_classification_report.pdf: Report documenting the classification analysis results.
  • README.md: This file providing an overview of the repository.

Instructions

  1. Data Preparation: Ensure iris_data.csv is in the appropriate directory.
  2. SPSS Analysis: Open iris_analysis_syntax.sps in SPSS and execute the syntax to perform analysis.
  3. Review Results: Refer to iris_classification_report.pdf for detailed analysis results and insights.

Usage

Feel free to clone this repository and use the provided resources for your own analysis or experimentation.

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Explore and analyze Iris dataset with SPSS for comprehensive classification insights. Implement statistical methods for robust data classification and gain valuable patterns from this well-known dataset

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