This is an introductory article for people who are curious about machine learning.
Here are a few examples:
Develop Business Case
You also learn that most of a data scientists time is actually spent on acquiring, cleaning, and exploring data. Pradeep Menon of Alibaba Cloud estimates 80% of your time is spent there, while only 20% is on modeling, deploying, and evaluating.
2. Unsupervised Learning.
3. Reinforcement Learning.
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
Clean and Explore Data
You might start by comparing the correlation of time on website and yearly amount spent.
sns.jointplot(x=’Time on Website’,y=’Yearly Amount Spent’,data=customers)
Linear plot using seaborn:
from sklearn.linear_model import LinearRegression
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
lm = LinearRegression()
print(‘Coefficients: \n’, lm.coef_)
[0.17825135 43.86015875 0.34813890 63.04039211]
Predict Test Data