Predicting-Sales-of-Video-Games.

Analysis and Prediction of Video Games sales using Machine Learning Algorithms !

Well, Gamers don't die, they Respawmn ;)

Video games have been around for decades, providing entertainment for children and adults alike.

They have evolved significantly from the early days of computer games and the first versions of Nintendo and Atari. The days of pixelated screens and limited sounds are a distant memory as video games have become more lifelike than ever.As technology continues to improve, so do video games.

Video game creation has become increasingly complex, and the cost of creating a game to run on one of the major consoles has risen with this greater complexity. It was once unthinkable to sink millions into development costs, but games today can cost tens and even hundreds of millions. This has pushed game development into Hollywood movie territory in terms of production and marketing costs.

The video game sector is immensely large. In fact, it is larger than the movie and music industry combined, and it is only growing. Though it doesn't get the same attention that the movie and music industry does, there are over two billion gamers across the world. That is 26% of the world's population.

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.

For Visualizations, i used :

• PLOTLY was used mainly and some
• Visualizations were made by using Matplotlib and Seaborn Libraries..!!

Did Data Pre-Processing :

• 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!

Extra 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!!!

Random Forest Regressor

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

XGBoost 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 four trained Models, It can be seen that the Linear Regressor model performed better than rest of the Models.

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