Page 293 - Touhpad Ai

plt.show()

# Step 5: Print regression equation
slope = model.coef_[0]
intercept = model.intercept_
print("\nLinear Regression Equation:\nPackage = ",round(slope,2), "× CGPA + ",
round(intercept,2))
Output:

Linear Regression Equation:
Package = 0.57 × CGPA + -0.99

Next, let us test the above model by accepting a new CGPA from the user and then displaying the predicted salary
package:

# Step 6: Accept user input and predict package
cgpa_input = float(input("\nEnter CGPA to predict the placement package: "))
predicted_package = model.predict([[cgpa_input]])[0]
print("Predicted Package: ₹",round(predicted_package,2),"LPA")
Output:
Enter CGPA to predict the placement package: 9.24
Predicted Package: ₹ 4.28 LPA
Project 2
This project predicts used car prices using a dataset that contains different car attributes such as year, power, engine
size, and kilometers driven. After cleaning and performing visualization on the dataset, a simple linear regression model
is applied to determine how various features affect car prices after the data has been cleaned and visualized. Download
the dataset from - https://www.kaggle.com/datasets/nehalbirla/vehicle-dataset-from-cardekho or scan the QR code.
The folder contains multiple files. We will be using car details v4.csv as our dataset.

# Import libraries
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

Project Work 291