2.8. Building a Logistic Regression Model

Building a logistic regression model using pandas and sklearn is very similar building a linear regression model. Let’s first look at our dataset. pass_fail.csv

import pandas as pd

data = pd.read_csv("pass_fail.csv")
print(data)

Output

Time Spent Studying (hours)  Exam Result
0                          4.5            1
1                          8.0            1
2                          1.5            0
3                          3.5            1
4                          5.5            1
5                          3.0            0
6                          6.5            1

Now we can build our logistic regression model. Instead of importing LinearRegression, we import LogisticRegression.

from sklearn.linear_model import LogisticRegression
import pandas as pd

data = pd.read_csv("pass_fail.csv")
x = data["Time Spent Studying (hours)"].to_numpy()
y = data["Exam Result"].to_numpy()

logistic_reg = LogisticRegression()
logistic_reg.fit(x.reshape(-1, 1), y)

beta0 = logistic_reg.intercept_
beta1 = logistic_reg.coef_[0]
print(beta0)
print(beta1)

Output

[-3.10848094]
[1.02761712]

We can also visualise our model.

from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# Load data
data = pd.read_csv("pass_fail.csv")
x = data["Time Spent Studying (hours)"].to_numpy()
y = data["Exam Result"].to_numpy()

# Build logistic regression model
logistic_reg = LogisticRegression()
logistic_reg.fit(x.reshape(-1, 1), y)

beta0 = logistic_reg.intercept_
beta1 = logistic_reg.coef_[0]

# Create x and y values to visualise the model function
x_model = np.linspace(0, 10, 50)
y_model = 1 / (1 + np.exp(-(beta0 + beta1 * x_model)))

plt.figure(figsize=(4, 4))
plt.scatter(x, y)  # Data
plt.plot(x_model, y_model, color="red")  # Model
plt.xlabel("Amout of Time Spent Studying (hours)")
plt.ylabel("Probability of a Pass")
plt.xlim([0, 10])
plt.ylim([-0.1, 1.1])
plt.tight_layout()
plt.savefig("plot.png")

Output

Code Challenge: Build a Logistic Regression Model

You have been provided with a csv file called rain.csv with data obtained from Kaggle . This data contains the following columns:

• Sunshine

• Humidity

• RainTomorrow

We will use the Sunshine column (the number of sunshine hours in a given day) to develop a logistic regression model that can help predict whether it’s going to rain tomorrow.

Instructions

1. Using pandas, read the file rain.csv into a DataFrame

2. Extract the 'Sunshine' column into the variable x

3. Extract the 'RainTomorrow' column into the variable y

4. Convert both x and y to numpy arrays

5. Using sklearn, create a LogisticRegression model to fit the training data

6. Create x and y values to visualise the model.

   • Use np.linspace(-10, 20, 100) to create an array of x values

   • Use the formula \(\cfrac{1}{1+e^{-(\beta_0 + \beta_1 x)}}\) to create a corresponding set of y values

7. Produce a figure that:

   • Has figsize (5, 4)

   • Plots the data as a scatter plot, alpha = 0.1

   • Plots the linear regression as a line, in red

   • Has labels Hours of Sunshine and Probability of Rain

Your plot should look like this:

Solution

Solution is locked