OliveML 🧠

Your comprehensive guide to machine learning, mathematics, and programming. import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression

Load and explore data

data = pd.read_csv(‘dataset.csv’) print(data.head()) print(data.info())

Prepare features and target

X = data.drop(‘target’, axis=1) y = data[‘target’]

Split data

X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42 )

Train model

model = LinearRegression() model.fit(X_train, y_train)

Evaluate

score = model.score(X_test, y_test) print(f”Model R² score: {score:.3f}“)

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• Mathematics - Linear algebra, calculus, statistics, and mathematical foundations
• Machine Learning - Algorithms, models, and practical implementations
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• About - Learn more about OliveML and our mission

*Start your journey in machine learning with structured, academic-quality tutorials designed for learners and researchers.*L”

OliveML 🧠

Your comprehensive guide to machine learning, mathematics, and programming.

Explore Our Content

• Mathematics - Linear algebra, calculus, statistics, and mathematical foundations
• Machine Learning - Algorithms, models, and practical implementations
• Python - Programming tutorials and data science with Python
• About - Learn more about OliveML and our mission

Start your journey in machine learning with structured, academic-quality tutorials designed for learners and researchers.