Use Linear Regression to predict on the price of backpacks

This month’s Kaggle competition is to make predictions on the price of backpacks. This competition is not as easy as it looks because I attempted several different methodologies to successfully predict the prices, and my attempts did not give the the results that I had hoped to achieve.

After two days and several submissions, I decided to put my work away for the time being, as I have exhausted the five attempts that I am allowed per day.

I have created the script in Kaggle’s free Jupyter Notebook and have saved it in my Kaggle account.

After identifying the problem statement, which is to predict on the price of backpaclks, I imported the libraries that I would need to execute the porogram, beingL-

1. Numpy to create numpy arrays and perform mathematical operations,
2. Pandas to create dataframes and process data,
3. Os to go into the operating system,
4. Sklearn to provide machine learning functionality,
5. Pylab to visualise the data,
6. Scipy to make a qq plot of the predicted data,
7. Matplotlib to visualise the data, and
8. Seaborn to statistically visualise the data.