Turning Academic Concepts Into Practical Excellence

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix

# Load dataset
data = pd.read_csv('student_data.csv')
X = data.drop('target', axis=1)
y = data['target']

# Academic Database Schema
CREATE TABLE Students (
  student_id INT PRIMARY KEY,
  name VARCHAR(100),
  email VARCHAR(100),
  major VARCHAR(50)
);

CREATE TABLE Projects (
  project_id INT PRIMARY KEY,
  title VARCHAR(200),
  description TEXT,
  student_id INT REFERENCES Students
);

public class MachineLearning {
  public static void main(String[] args) {
    // Neural network implementation
    NeuralNetwork nn = new NeuralNetwork();
    nn.addLayer(new Layer(10, 20));
    nn.addLayer(new Layer(20, 15));
    nn.addLayer(new Layer(15, 1));
    nn.train(dataset, 1000);
  }
}