Code Tutorials

import numpy as np
from sklearn.linear_model import LinearRegression

# Create sample data
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([2, 4, 6, 8, 10])

# Create and train model
model = LinearRegression()
model.fit(X, y)

# Make predictions
predictions = model.predict([[6]])

import pandas as pd

# Load and clean data
df = pd.read_csv('data.csv')
df = df.dropna()

# Feature engineering
df['new_feature'] = df['column_a'] / df['column_b']

# Normalize data
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
df_scaled = scaler.fit_transform(df)