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22. Find the Transpose of the following matrix.
2 –5 3
Ans. 2 –1 –1 0 2/5
–5 0
3 2/5
23. Find the sum of A and B.
16 –10 6 2
Ans. A = 5 0 B = –10 8
16 –10 6 2
A + B = 5 0 + –10 8
22 –8
= –5 8
24. Perform Linear Regression on the Mobile Phone Price Prediction (The dataset can be downloaded
from https://www.kaggle.com/datasets/dewangmoghe/mobile-phone-price-prediction save the
file in csv format by the name of “mobile phone price prediction”. Also display few records of the
y-predict variable of testing dataset after linear regression has been performed.
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
# Load the dataset
data = pd.read_csv('mobile phone price prediction.csv')
# Extract numerical values from the 'Battery' and 'Price' columns
data['Battery'] = data['Battery'].str.extract('(\d+)').astype(int)
data['Price'] = data['Price'].str.replace(',', '').astype(int)
# Select the feature and target variable
X = data[['Battery']] # Feature: Battery
y = data['Price'] # Target: Price
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_
state=42)
# Create and train the linear regression model
model = LinearRegression()
model.fit(X_train, y_train)
# Predict the target variable for the testing set
y_pred = model.predict(X_test)
# Print the first 10 rows of the actual and predicted values
print("First 10 actual prices vs predicted prices:")
for actual, predicted in zip(y_test[:10], y_pred[:10]):
print(f"Actual: {actual}, Predicted: {predicted}")
# Plot the results
plt.scatter(X_test, y_test, color='blue', label='Actual prices')
plt.plot(X_test, y_pred, color='red', label='Predicted prices')
plt.xlabel('Battery Power (mAh)')
plt.ylabel('Price (INR)')
plt.title('Battery Power vs. Price')
440 Touchpad Artificial Intelligence (Ver. 3.0)-XI

