Iris-Classification

This analysis the Iris dataset to classify species using scaled features using classification

Problem

• Analyze Iris dataset and apply feature scaling using StandardScaler.

Data Dictionary

Column	Description
sepal_length	Length of the sepal of the flower (cm).
sepal_width	Width of the sepal of the flower (cm).
petal_length	Length of the petal of the flower (cm).
petal_width	Width of the petal of the flower (cm).
species	Species of the iris flower (setosa, versicolor, virginica).

Summary

Data Cleaning

No data cleaning had to be preformed on this dataset, no missing value, NaN values or special characters were present in the dataset, outliers were deteched

Key Visualization

This include key visualizations that highlight important aspects of the data.

Visualization 1:

This pairplot shows the difference featueres for the petals and sepals. It shows the pairplot of each features

Visualization 2:

This charts shows the distrubtion between species across the different flowers (setosa, versicolor, virginica)

Visualization 3:

Lasly, this heatmap shows the correlation between the sepal_length, sepal_width and petal_length, petal_width.

As you can see from the heatmap, the strongest correlation is between petal witdh and petal height with 0.96 correlation.

Conclusions/Recommendations

the dataset was trained on with the iris dataset

• KNeighbors Regression
• Linear Regression
• Random Forest Regression

The result of the models

Model	Accuracy
KNeighbors	0.82
Linear Regression	0.79
Random Forest	0.99

Here the best score here is the Random Forest was trained on the four features.