# Sklearn Logistic Regression | Logistic Regression Python

Logistic Regression is a supervised machine-learning algorithm. Logistic regression is used for classification problems. It predicts the probability of the target variable. We can quickly implement logistic regression in Python using the Sklearn library. You have to follow the given steps to implement the logistic regression.

Step 1: Import the libraries

`import seaborn as sns`

Step 2: Import the iris dataset

```iris_data = sns.load_dataset("iris")
```   sepal_length  sepal_width  petal_length  petal_width species
0           5.1          3.5           1.4          0.2  setosa
1           4.9          3.0           1.4          0.2  setosa
2           4.7          3.2           1.3          0.2  setosa
3           4.6          3.1           1.5          0.2  setosa
4           5.0          3.6           1.4          0.2  setosa```

Step 3: Split the dataset into the Training set and Test set

```from sklearn.model_selection import train_test_split
X = iris_data.iloc[:, :-1].values
y = iris_data.iloc[:, 4].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)```

Step 4: Feature Scaling

```from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)```

Step 5: Fitting Logistic Regression to the Training set using Sklearn

```from sklearn.linear_model import LogisticRegression
model = LogisticRegression(random_state = 0)
model.fit(X_train, y_train)```

Step 6: Prediction on the Test set

`y_pred = model.predict(X_test)`

Step 7: Accuracy on the training set and test set

```from sklearn.metrics import accuracy_score
print("Accuracy on training set: ", accuracy_score(y_train, model.predict(X_train)))
print("Accuracy on test set", accuracy_score(y_test, y_pred))```
```Accuracy on training set:  0.9732142857142857
Accuracy on test set 0.9736842105263158```

Step 8: Confusion Matrix

```from sklearn.metrics import confusion_matrix
c_matric = confusion_matrix(y_test, y_pred)
print(c_matric)```
```[[13  0  0]
[ 0 15  1]
[ 0  0  9]]```

Complete Code:

```# Step 1: Import the libraries
import seaborn as sns

# Step 2: Import the iris dataset

# Step 3: Split the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

X = iris_data.iloc[:, :-1].values
y = iris_data.iloc[:, 4].values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)

# Step 4: Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

# Step 5: Fitting Logistic Regression to the Training set
from sklearn.linear_model import LogisticRegression
model = LogisticRegression(random_state = 0)
model.fit(X_train, y_train)

# Step 6: Prediction on Test set
y_pred = model.predict(X_test)

# Step 7: Accuracy on training set and test set
from sklearn.metrics import accuracy_score
print("Accuracy on training set: ", accuracy_score(y_train, model.predict(X_train)))
print("Accuracy on test set", accuracy_score(y_test, y_pred))

# Step 8: Confusion Matrix
from sklearn.metrics import confusion_matrix
c_matric = confusion_matrix(y_test, y_pred)
print(c_matric)```

Output:

```Accuracy on training set:  0.9732142857142857
Accuracy on test set 0.9736842105263158
[[13  0  0]
[ 0 15  1]
[ 0  0  9]]```

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