Challenge

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Task

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In this task, you build, train and fit your model and make predictions based on it. This time you will make predictions about total_phenols, based on flavanoids. It means that your target now is total_phenols.

Your plan:

[Line #18] Define the target (in this task it's total_phenols).
[Line #25] Split the data 70-30 (70% of the data is for training and 30% is for testing) and insert 1 as a random parameter.
[Line #26] Initialize linear regression model .
[Line #27] Fit the model using your tain data.
[Line #30] Assign np.array() to the variable new_flavanoids if their number is 1 (don't forget to use function .reshape(-1,1)).
[Line #31] Predict and assign the amount of flavanoids to the variable predicted_value.
[Line #32] Print the predicted amount of flavanoids.

Solution

# Import the libraries

import pandas as pd

from sklearn.datasets import load_wine

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LinearRegression

import numpy as np

# Load the dataset

wine = load_wine()

# Configure pandas to show all features

pd.set_option('display.max_rows', None, 'display.max_columns', None)

# Define the DataFrame

data = pd.DataFrame(data = wine['data'], columns = wine['feature_names'])

# Define the target

data['total_phenols'] = wine.target

# Define the data we will work with

x = data[['flavanoids']]

y = data['total_phenols']

# Build and fit the model

X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size = 0.3, random_state = 1)

model = LinearRegression()

model.fit(X_train, Y_train)

# Make predictions

new_flavanoids = np.array([1]).reshape(-1, 1)

predicted_value = model.predict(new_flavanoids)

print(predicted_value)

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 4