Introduction to Machine Learning

Lesson Introduction

Machine learning! You’ve probably heard this term a lot. But what exactly is it? Think of it as teaching a computer to learn from data and make decisions or predictions based on that data. This is like teaching a child to recognize different objects by showing them examples. In this lesson, our goal is to understand the basics of a machine learning project. We’ll generate data, visualize it, and understand the relationships within it.

Data Generation

Let’s start by generating some data. In real-life projects, the first step is to collect data, but we'll create synthetic (fake) data for our learning purposes using NumPy . Why random data? It simulates different scenarios and creates a controlled environment for learning. Don't worry, in the end of this course we will work with the real data as well. We'll use NumPy to generate areas of houses (in square feet) and their prices: Python import numpy as np # Set random seed for reproducibility np.random.seed(42) # Generate synthetic data num_samples = 100 area = np.random.uniform(500, 3500, num_samples) # House area in square feet area = np.round(area, 2) # Round to 2 decimal places # Assume a linear relationship: price = base_price + (area * price_per_sqft) base_price = 50000 price_per_sqft = 200 noise = np.random.normal(0, 25000, num_samples) # Adding some noise price = base_price + (area * price_per_sqft) + noise price = np.round(price, 2) # Round to 2 decimal places # Display a few generated data points for verification print("Area (sq ft):", area[:5]) # Area (sq ft): [1623.62 3352.14 2695.98 2295.98 968.06] print("Price ($):", price[:5]) # Price ($): [376900.18 712952.82 591490.02 459506.78 238120.2] Real-life example: Imagine you want to predict house prices in your neighborhood. The area of the house affects the price. We simulate this by creating a simple linear relationship but add noise to make it realistic. Let's break down the data generation: Generate House Areas : Creates 100 random house areas between 500 and 3500 square feet. Define Price Relationship : Base price: A constant starting price. Price per square foot: A fixed price per unit area. Noise: Adds variability to simulate real-world data. Calculate Prices : Computes the final prices based on the area, base price, price per square foot, and added noise. This method creates a realistic dataset with variable house prices based on their areas.

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Python

import numpy as np

# Set random seed for reproducibility
np.random.seed(42)

# Generate synthetic data
num_samples = 100
area = np.random.uniform(500, 3500, num_samples)  # House area in square feet
area = np.round(area, 2)  # Round to 2 decimal places

# Assume a linear relationship: price = base_price + (area * price_per_sqft)
base_price = 50000
price_per_sqft = 200
noise = np.random.normal(0, 25000, num_samples)  # Adding some noise
price = base_price + (area * price_per_sqft) + noise
price = np.round(price, 2)  # Round to 2 decimal places

# Display a few generated data points for verification
print("Area (sq ft):", area[:5])  # Area (sq ft): [1623.62 3352.14 2695.98 2295.98  968.06]
print("Price ($):", price[:5])  # Price ($): [376900.18 712952.82 591490.02 459506.78 238120.2]

Python

import pandas as pd

# Create DataFrame
data = pd.DataFrame({'Area': area, 'Price': price})

# Display first few rows of the dataset
print(data.head())

import pandas as pd

# Create DataFrame
data = pd.DataFrame({'Area': area, 'Price': price})

# Display first few rows of the dataset
print(data.head())

      Area      Price
0  1623.62  376900.18
1  3352.14  712952.82
2  2695.98  591490.02
3  2295.98  459506.78
4   968.06  238120.20

      Area      Price
0  1623.62  376900.18
1  3352.14  712952.82
2  2695.98  591490.02
3  2295.98  459506.78
4   968.06  238120.20

Python

import matplotlib.pyplot as plt

# Plot the data to visualize the relationship
plt.scatter(data['Area'], data['Price'], alpha=0.5)
plt.title('House Area vs. Price')
plt.xlabel('Area (sq ft)')
plt.ylabel('Price ($)')
plt.grid()
plt.show()

import matplotlib.pyplot as plt

# Plot the data to visualize the relationship
plt.scatter(data['Area'], data['Price'], alpha=0.5)
plt.title('House Area vs. Price')
plt.xlabel('Area (sq ft)')
plt.ylabel('Price ($)')
plt.grid()
plt.show()

Python

import pandas as pd

# Create DataFrame
data = pd.DataFrame({'Area': area, 'Price': price})

# Display first few rows of the dataset
print(data.head())

      Area      Price
0  1623.62  376900.18
1  3352.14  712952.82
2  2695.98  591490.02
3  2295.98  459506.78
4   968.06  238120.20

Python

import matplotlib.pyplot as plt

# Plot the data to visualize the relationship
plt.scatter(data['Area'], data['Price'], alpha=0.5)
plt.title('House Area vs. Price')
plt.xlabel('Area (sq ft)')
plt.ylabel('Price ($)')
plt.grid()
plt.show()

Lesson Introduction

Data Generation

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Creating a Data Structure

Data Visualization

Lesson Summary