Building Machine Learning Models with Python

Learn how to build machine learning models using scikit-learn

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Machine learning is a branch of artificial intelligence that enables computers to learn from data and make predictions or decisions without being explicitly programmed. Python’s scikit-learn library is a powerful tool for building machine-learning models, providing a simple and efficient way to implement complex algorithms.

Let’s look at how to build machine learning models using scikit-learn.

Preparation

Before we dive into building models, we need to prepare the data. This typically involves loading the data into a pandas DataFrame, splitting it into training and testing sets, and preprocessing the data to remove any missing values and perform feature scaling.

Here’s a code snippet to demonstrate the data preparation process:

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# Load data into pandas DataFrame
df = pd.read_csv("data.csv")

# Split data into training and testing sets
X = df.drop("target", axis=1)
y = df["target"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Preprocess data
scaler = StandardScaler()
X_train =…