The Iris dataset analysis project involves loading and preprocessing the dataset, followed by exploratory data analysis with histograms, scatter plots, and a correlation heatmap. This project provides a comprehensive approach to analyzing and modeling the Iris dataset for classification.
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iris-data-analysis's Introduction
Iris dataset analysis - Classification
Dataset Information
The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other.
Download link:https://www.kaggle.com/uciml/iris
Step 1: Import Modules
Import libraries for data manipulation (pandas, numpy), visualization (matplotlib, seaborn), and machine learning (scikit-learn).
Step 2: Load Dataset and Derive Insights
Load the Iris dataset.
Display the first few rows to understand the structure.
Get dataset info (data types, null values).
Generate statistical summary (mean, median, standard deviation).
Step 3: Preprocess the Dataset (Removing Null Values)
Check for and remove any null values to ensure a clean dataset.
Step 4: Exploratory Data Analysis (Histogram and Scatter Plot)
Create histograms for feature distribution.
Generate pair plots to visualize relationships between features, categorized by species.
Step 5: Correlation Matrix (Heat Map)
Compute and visualize the correlation matrix using a heat map to identify relationships between features.
Step 6: Label Encoder
Convert categorical species labels into numeric form using a label encoder for machine learning compatibility.
Step 7: Model Training
Split data into features and target, then into training and testing sets.
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