Which-Laptop-to-buy, Analysis!

Laptop data and specification analysis as a Buying guide and Price Prediction using Machine Learning Algorithms !

To join in the industrial revolution, you needed to open a factory; in the Internet revolution, you need to open a laptop.

Everything will be okay. I have a sticker on my laptop that says that.

Laptops are compact enough to carry with you, yet versatile enough to run demanding applications. Notebooks are the best tool for doing serious work or play whether you're at home, on the road or in a college classroom.

While standalone tablets and smartphones are always popular, most people realize That everything from typing a research paper to crunching videos to gaming works etter on a laptop. So what type of laptop should you get?

There's a wide variety of sizes, features and prices, which makes choosing the best laptop a challenge. That's why you need to figure out what your needs are.

Laptops offer many advantages. They are small and light enough to be portable, yet still powerful enough to run complex, demanding programs. They offer capabilities and a degree of usefulness not found in even the best tablet or smartphone.

With an almost unlimited number of options for a large variety of use cases, equipping and buying the right laptop, tablet, or mobile device can be a daunting and confusing challenge. Whether you need to buy one or multiple laptops, for business or personal use, a laptop buying guide will help you make a wise, informed purchasing decision.

For Solving this Usecase, What I have done is :

• Collected the data and organized it to form a meaningful dataset.
• Checked for null values and took care of it.
• Observed the data to form meaningful insights!
  
  
• Did Exploratory Data Analysis on the dataset.
• Used correlations to form a heatmap.
• Did some Data Cleaning as well.

For Visualizations, i used :

• Visualizations were made by using Matplotlib and Seaborn Libraries..!!

Did Data Pre-Processing and Feature Engineering :

• Made dummies for improving my model's Performance.
• Made Binary Classifications Using Label Encoder
• and Standard Scaler
  To fit and transform Numerical and Categorical Column values.

And then I made my model for the Prediction :

• Did data processing
• Independent and Dependent Features.
• Did Train-Test split

Trained my Model using :

Linear Regressor

• Fitted the model.
• predicted the test scores and checked the same.
• Plotted the prediction.
• Prediction plot gave a Normal Distribution curve.
• Plotted the Best fit line for the model.
• Calculated Mean Absolute error and Root Mean Squared Error!

Random Forest Regressor

• Fitted the model.!!!!
• predicted the test scores.
• Plotted the prediction.
• Prediction plot gave a Normal Distribution curve.
• Plotted the Best fit line for the model.
• Calculated Mean Absolute error and Root Mean Squared Error!

Decision Trees Regressor

• Fitted the model.
• predicted the test scores.
• Plotted the prediction.
• Prediction plot gave a Normal Distribution curve.
• Plotted the Best fit line for the model...
• Calculated Mean Absolute error and Root Mean Squared Error!

And for the conclusion -

From the above three trained Models, It can be seen that the Decision Trees Regressor model performed better than rest of the Models.

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