Iris Classification Project

The purpose of this project is to illustrate the classification model of Iris flowers.

LOAD DATASET

First of all, we upload our data.

from sklearn.datasets import load_iris dataSet = load_iris()

features = dataSet.data labels = dataSet.target
labelsNames = list(dataSet.target_names) featuresNames = dataSet.feature_names

print([labelsNames[i] for i in labels[47:52]])
print(featuresNames)

ANALAYZE DATA

We use Pandas to analyze the data we upload.

import pandas as pd

print(type(features))

featuresDF= pd.DataFrame(features) featuresDF.columns = featuresNames

print(type(featuresDF)) print(featuresDF.describe())
print(featuresDF.info())

VISUALIZE DATA

Then we visualized the data we uploaded.

featuresDF.plot(x="sepal lenght" (cm), y= "sepal width (cm)", kind= "scatter")

or

featuresDF.plot(kind= "bar")

You can access more from https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html

SELECT MODEL

First we chose our model from https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html, then we selected it as KNeighborsClassifier and ran it with parameters.

from sklearn.neighbors import KNeighborsClassifier

clf = KNeighborsClassifier(n_neighbors=8)

SPLIT DATASET

After choosing our model, we divided our data with train_test_split.

import numpy as np
from sklearn.model_selection import train_test_split X, y = np.arange(10).reshape((5, 2)), range(5)

X = features y = labels

X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=42)

Now we have training and testing data and the right labels.

TRAIN MODEL

clf.fit(X_train, y_train)

accuarcy = clf.score(X_train,y_train)

print("accuarcy on train data {:.2}%".format(accuarcy))

We have seen the success of this model we have trained.

TEST MODEL

We carry out the test

accuarcy = clf.score(X_test,y_test)

print("accuarcy on test data {:.2}%".format(accuarcy))

SAVE MODEL

We stored the model using .joblib. we loaded the same function into a new variable with load while storing it with dump.

from joblib import dump, load filename = "myFirstSavedModel.joblib" dump(clf, filename)

clfUploaded = load(filename)

TEST WITH UPLOADED MODEL

We're testing this model again with the same data set

accuarcy = clfUploaded.score(X_test,y_test)

print("accuarcy on test data {:.2}%".format(accuarcy))