What are the main steps and components needed to build a simple machine learning model from scratch during development?

To build a simple machine learning model from scratch, the following steps are usually performed:

1. Data collection and preparation — obtaining data, cleaning, handling missing values, normalization or standardization.
2. Data splitting — dividing into training and testing sets (e.g., 80/20).
3. Model selection — determining the type of model (linear regression, decision tree, etc.).
4. Model training — tuning model parameters on training data.
5. Model evaluation — testing the model on test data using metrics (accuracy, RMSE, etc.).
6. Model deployment — applying the trained model to make predictions on new data.

Example in Python using simple linear regression without external libraries:

# Simple linear regression example
import numpy as np

# Data
X = np.array([1, 2, 3, 4, 5])
y = np.array([2, 4, 5, 4, 5])

# Add a column of ones for the intercept term
X_b = np.c_[np.ones((len(X), 1)), X]

# Least squares method to compute coefficients
theta_best = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y)

print(f"Model coefficients: {theta_best}")

# Prediction
X_new = np.array([[1], [6]])
X_new_b = np.c_[np.ones((2, 1)), X_new]
predictions = X_new_b.dot(theta_best)
print(f"Predictions: {predictions}")

This example demonstrates the basic components: data preparation, training, and prediction.