In this Challenge, you are given the following dataset:

import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/circles.csv')
print(df.head())

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/circles.csv')
plt.scatter(df['X1'], df['X2'], c=df['y'])

The dataset is for sure not linearly separable. Let's look at the Logistic Regression performance:

import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/b71ff7ac-3932-41d2-a4d8-060e24b00129/circles.csv')
X = df[['X1', 'X2']]
y = df['y']

X = StandardScaler().fit_transform(X)
lr = LogisticRegression().fit(X, y)
print(cross_val_score(lr, X, y).mean())

The result is awful. Regular Logistic Regression is not suited for this task. Your task is to check whether the `PolynomialFeatures` will help. To find the best `C` parameter, you will use the `GridSearchCV` class.

In this challenge, the `Pipeline` is used. You can think of it as a list of preprocessing steps. Its `.fit_transform()` method sequentially applies `.fit_transform()` to each item.

Classification with Python

Challenge

Task