Regression

Regression:

Regression in supervised learning is a statistical method used to understand and model the relationship between independent variables (inputs) and a dependent variable (output) that is continuous in nature. It helps in predicting outcomes or trends by fitting the best possible line or curve through the data points. The main goal of regression is to minimize the difference between the predicted values and the actual values.

Example

Suppose a shopkeeper wants to predict the sales of ice cream based on the temperature outside. He collects past data showing that on hotter days, sales are higher, and on cooler days, sales are lower. Using regression, he can build a model that learns this relationship and then predict future sales if the weather forecast says the temperature will be, say, 35°C.

What is a table?

A table is a structured way to organize data into rows and columns so that algorithms can process it effectively. Each row represents a data instance (or example), and each column represents a feature (or attribute) of that instance. Tables are commonly used in machine learning for tasks like training models, making predictions, or analyzing patterns.

Examples of tables in coding

!pip install datascience

The command !pip install datascience is used to install the datascience library in Python, which provides tools for organizing, analyzing, and visualizing data. In terms of learning, this is like getting a toolkit or textbook that helps you understand data more easily. Once installed, you can use features like tables, charts, and statistical functions to explore data, find patterns, and practice concepts in AI or machine learning. Without installing the library, these tools wouldn’t be available, so this step is essential for learning and experimenting with data effectively.

from sklearn.preprocessing import PolynomialFeatures

from sklearn.linear_model import LinearRegression

import matplotlib.pyplot as plt

import numpy as np

# Prepare data for polynomial regression

ages = combined_population_2019.column('AGE').reshape(-1, 1)

total_population = combined_population_2019.column('Total 2019')

# Generate polynomial features (degree 10)

poly_features = PolynomialFeatures(degree=10)

ages_poly = poly_features.fit_transform(ages)

# Perform linear regression with polynomial features

model = LinearRegression()

model.fit(ages_poly, total_population)

# Generate predicted values across the age range

ages_for_prediction = np.linspace(ages.min(), ages.max(), 100).reshape(-1, 1)

ages_for_prediction_poly = poly_features.transform(ages_for_prediction)

predicted_population_poly = model.predict(ages_for_prediction_poly)

# Plot the original data and the polynomial regression curve

combined_population_2019.plot('AGE')

plt.plot(ages_for_prediction, predicted_population_poly, color='red', label=f'Polynomial Regression (Degree 10)')

plt.xlabel('Age')

plt.ylabel('Population (2019)')

plt.title('Population vs Age with Polynomial Regression')

plt.legend()

plt.show()