Alright, let's talk about cars! 🚗 We've got a bunch of info about them, like how much they cost and what color they are. But here's the thing: this data is like a locked treasure chest. To open it up and find cool stuff, we need to do something called Data Analysis!

Now, picture this: Your friend wants to sell his car, but he's not sure how much to ask for it. He wants to get a good deal and make some money, but he also wants it to be fair for the person buying it. That's where you come in!

Think of yourself as a detective, but for data. We've got some questions to solve. Do we know how much other cars with different colors or brands cost? Does the car's power (horsepower) affect the price, or maybe something else?

As data experts, these are the questions we're here to answer. But to do that, we need one thing: data! 📊💡

• Import required modules
• Define dataset column headers
• Check for missing values
• Checking datatypes for each column of the dataset
• Normalizing values using feature scaling method and binning-grouping values
• Performing various data cleaning and data visualization operations on your data

• Pandas
• Matplotlib
• NumPy
• Seaborn
• Scipy

[Status: Archived]