Practical Data Science using Python - Linear Regression Car Price Prediction

The video tutorial covers the application of linear regression to predict car prices based on historical sales data. It begins with an introduction to the problem, followed by the import of essential Python libraries like Numpy, Pandas, Matplotlib, and Seaborn for data analysis and visualization. The tutorial then delves into exploratory data analysis, handling categorical data, and performing statistical analysis to identify outliers. Finally, it addresses data cleaning by correcting anomalies and checking for duplicates to ensure data integrity.

What is the main goal of the car sales agency's model?

• To predict future car prices

• To automate car manufacturing

• To predict the number of cars sold

• To determine the best car brand

Which library is primarily used for data frame management in Python?

• Numpy

• Pandas

• Matplotlib

• Seaborn

What is the first step in setting up the environment for data analysis?

• Importing necessary libraries

• Collecting data

• Visualizing data

• Building the model

Why is it important to convert categorical features into numeric variables for linear regression?

• To improve data visualization

• To reduce the size of the dataset

• To make the data more readable

• Because linear regression only works with numeric data

Which of the following is a categorical feature in the car dataset?

• Horsepower

• Car body type

• Compression ratio

• Wheelbase

What does the 'describe' function in Python provide?

• A list of all data types

• Statistical information about numeric features

• A visualization of the dataset

• A summary of all categorical features

What should be done if null values are present in the dataset?

• Ignore them

• Drop the entire dataset

• Convert them to zero

• Use the dropna function

What is the purpose of checking for duplicate entries in a dataset?

• To improve data visualization

• To ensure data consistency

• To reduce processing time

• To increase the dataset size

Why is it important to correct spelling mistakes in the dataset?

• To make the dataset look neat

• To ensure accurate model training

• To reduce the dataset size

• To improve data visualization

What is the purpose of splitting the car name to extract the company name?

• To simplify data preprocessing

• To focus on brand analysis

• To reduce the dataset size

• To improve data visualization