Créer son premier modèle en Scikit-Learn¶

Chargement des données¶

In [1]:
# Import du fichier CSV
import pandas as pd

iris_df = pd.read_csv('iris.csv')
In [2]:
# Affichage des premières lignes
iris_df.head()
Out[2]:
sepal_length sepal_width petal_length petal_width class
0 5.1 3.5 1.4 0.2 Iris-setosa
1 4.9 3.0 1.4 0.2 Iris-setosa
2 4.7 3.2 1.3 0.2 Iris-setosa
3 4.6 3.1 1.5 0.2 Iris-setosa
4 5.0 3.6 1.4 0.2 Iris-setosa
In [3]:
# Taille du dataset sous la forme (nb lignes, nb colonnes)
iris_df.shape
Out[3]:
(150, 5)

Analyse du dataset¶

In [4]:
# Statistiques descriptives
iris_df.describe()
Out[4]:
sepal_length sepal_width petal_length petal_width
count 150.000000 150.000000 150.000000 150.000000
mean 5.843333 3.054000 3.758667 1.198667
std 0.828066 0.433594 1.764420 0.763161
min 4.300000 2.000000 1.000000 0.100000
25% 5.100000 2.800000 1.600000 0.300000
50% 5.800000 3.000000 4.350000 1.300000
75% 6.400000 3.300000 5.100000 1.800000
max 7.900000 4.400000 6.900000 2.500000
In [5]:
# Histogramme des longueurs de pétales
iris_df['petal_length'].hist(bins=30)
Out[5]:
<Axes: >
In [6]:
# Graphique comparant les tailles des pétales par espèce (boxplot)
iris_df.boxplot(by='class', column=['petal_length'])
Out[6]:
<Axes: title={'center': 'petal_length'}, xlabel='class'>

Préparation des données¶

In [7]:
# Données manquantes ?
iris_df.isna().sum()
Out[7]:
sepal_length    0
sepal_width     0
petal_length    0
petal_width     0
class           0
dtype: int64

Entrainement de modèles et évaluation¶

In [8]:
# Séparation cible (y) vs variables explicatives (X)
y = iris_df['class']
X = iris_df.drop(['class'], axis=1)
In [9]:
# Séparation train vs test
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
In [10]:
# Standardisation des données
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
In [11]:
# Entrainement
from sklearn.tree import DecisionTreeClassifier

tree = DecisionTreeClassifier(max_depth=2)
tree.fit(X_train, y_train)
y_pred = tree.predict(X_test)
In [12]:
# Rapport de classification
from sklearn.metrics import classification_report

print(classification_report(y_test, y_pred))

                 precision    recall  f1-score   support

    Iris-setosa       1.00      1.00      1.00        10
Iris-versicolor       1.00      0.89      0.94         9
 Iris-virginica       0.92      1.00      0.96        11

       accuracy                           0.97        30
      macro avg       0.97      0.96      0.97        30
   weighted avg       0.97      0.97      0.97        30
In [13]:
# Matrice de confusion
from sklearn.metrics import confusion_matrix

confusion_matrix(y_test, y_pred)
Out[13]:
array([[10,  0,  0],
       [ 0,  8,  1],
       [ 0,  0, 11]])
In [ ]: