Sure, I can provide two real-life examples of regression along with beginner-friendly Python code. But first, let's understand what regression is.

**Regression** is a statistical method used in finance, investing, and other disciplines that attempts to determine the strength and character of the relationship between one dependent variable (usually denoted by Y) and a series of other variables (known as independent variables).

**Key Points of Regression:**
1. It helps in predicting a continuous value.
2. It shows the impact of change of independent variables on the dependent variables.
3. The dependent variable is the main factor that you want to understand or predict.

**Example 1: Predicting House Prices**

Let's say we want to predict the price of a house based on its size. Here, the price is the dependent variable (Y) and the size is the independent variable.

```python
# Importing Required Libraries
from sklearn.linear_model import LinearRegression
import numpy as np

# Data (Size in sqft, Price in USD)
size = np.array([750, 800, 850, 900, 950])
price = np.array([200000, 220000, 240000, 260000, 280000])

# Reshaping the data
size = size.reshape(-1, 1)
price = price.reshape(-1, 1)

# Creating the model and fitting the data
model = LinearRegression()
model.fit(size, price)

# Predicting the price of a house of size 1000 sqft
predicted_price = model.predict(np.array([1000]).reshape(-1, 1))
print(predicted_price)
```

**Example 2: Predicting Salary based on Years of Experience**

Here, the salary is the dependent variable (Y) and the years of experience is the independent variable.

```python
# Importing Required Libraries
from sklearn.linear_model import LinearRegression
import numpy as np

# Data (Years of Experience, Salary in USD)
experience = np.array([1, 2, 3, 4, 5])
salary = np.array([30000, 35000, 40000, 45000, 50000])

# Reshaping the data
experience = experience.reshape(-1, 1)
salary = salary.reshape(-1, 1)

# Creating the model and fitting the data
model = LinearRegression()
model.fit(experience, salary)

# Predicting the salary of a person with 6 years of experience
predicted_salary = model.predict(np.array([6]).reshape(-1, 1))
print(predicted_salary)
```

In both examples, we are using simple linear regression where we have only one independent variable. The `LinearRegression()` function from the `sklearn.linear_model` library is used to perform linear regression.

Question

Sure, I can provide two real-life examples of regression along with beginner-friendly Python code. But first, let's understand what regression is.

**Regression** is a statistical method used in finance, investing, and other disciplines that attempts to determine the strength and character of the relationship between one dependent variable (usually denoted by Y) and a series of other variables (known as independent variables).

**Key Points of Regression:**
1. It helps in predicting a continuous value.
2. It shows the impact of change of independent variables on the dependent variables.
3. The dependent variable is the main factor that you want to understand or predict.

**Example 1: Predicting House Prices**

Let's say we want to predict the price of a house based on its size. Here, the price is the dependent variable (Y) and the size is the independent variable.

```python
# Importing Required Libraries
from sklearn.linear_model import LinearRegression
import numpy as np

# Data (Size in sqft, Price in USD)
size = np.array([750, 800, 850, 900, 950])
price = np.array([200000, 220000, 240000, 260000, 280000])

# Reshaping the data
size = size.reshape(-1, 1)
price = price.reshape(-1, 1)

# Creating the model and fitting the data
model = LinearRegression()
model.fit(size, price)

# Predicting the price of a house of size 1000 sqft
predicted_price = model.predict(np.array([1000]).reshape(-1, 1))
print(predicted_price)
```

**Example 2: Predicting Salary based on Years of Experience**

Here, the salary is the dependent variable (Y) and the years of experience is the independent variable.

```python
# Importing Required Libraries
from sklearn.linear_model import LinearRegression
import numpy as np

# Data (Years of Experience, Salary in USD)
experience = np.array([1, 2, 3, 4, 5])
salary = np.array([30000, 35000, 40000, 45000, 50000])

# Reshaping the data
experience = experience.reshape(-1, 1)
salary = salary.reshape(-1, 1)

# Creating the model and fitting the data
model = LinearRegression()
model.fit(experience, salary)

# Predicting the salary of a person with 6 years of experience
predicted_salary = model.predict(np.array([6]).reshape(-1, 1))
print(predicted_salary)
```

In both examples, we are using simple linear regression where we have only one independent variable. The `LinearRegression()` function from the `sklearn.linear_model` library is used to perform linear regression.

Knowee AI · Accepted Answer