Code Examples

Example of data preprocessing using pandas and scikit-learn:

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

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

# Handle missing values
df = df.fillna(df.mean())

# Feature scaling
scaler = StandardScaler()
X = scaler.fit_transform(df.drop('target', axis=1))
y = df['target']

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

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Example of training a Random Forest classifier:

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report

# Initialize and train model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Make predictions
y_pred = model.predict(X_test)

# Evaluate model
print(classification_report(y_test, y_pred))

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Example of a simple neural network using PyTorch:

import torch
import torch.nn as nn

class SimpleNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(SimpleNN, self).__init__()
        self.layer1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.layer2 = nn.Linear(hidden_size, output_size)
    
    def forward(self, x):
        x = self.layer1(x)
        x = self.relu(x)
        x = self.layer2(x)
        return x

# Initialize model
model = SimpleNN(input_size=10, hidden_size=64, output_size=2)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())

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