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Code templates and resources.txt
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Code templates and resources.txt
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# Import libraries
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
# Load the data
df = pd.read_csv('data.csv')
# Split the data into features and target
X = df.drop(['target'], axis=1)
y = df['target']
# Standardize the features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Split the data into train and test sets
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
# Import libraries
import seaborn as sns
import matplotlib.pyplot as plt
# Plot a histogram
sns.histplot(df['column_name'])
plt.show()
# Plot a scatterplot
sns.scatterplot(x='x_column', y='y_column', data=df)
plt.show()
# Plot a boxplot
sns.boxplot(x='column_name', y='y_column', data=df)
plt.show()
Here are some additional resources that readers can use to learn more about the topics covered in your notes:
Tutorials: Online tutorials and guides can provide step-by-step instructions on how to use different libraries and techniques. Some popular sites for finding tutorials include DataCamp, Kaggle, and Towards Data Science.
Documentation: The official documentation for libraries and frameworks can provide detailed explanations of their functions and features. For example, you can find the documentation for Pandas and Scikit-learn on their websites.
Online courses: Online courses and MOOCs (massive open online courses) can provide structured learning experiences with video lectures, quizzes, and projects. Some popular platforms for finding online courses include Coursera, edX, and Udemy.