prateekagr21 / Price-Analysis-of-Audi-Cars-across-Europe

Determining the Sales of Audi Cars across whole Europe by comparing the specifications as well as the price of some bestselling Models.

Price-Analysis-of-Audi-Cars-across-Europe

Audi Europe Cars Experience Analysis and Price Prediction using Machine Learning Algorithms !

The Four rings of Happiness!!!!

Audi European sales have risen steadily every year from 2009 to 2016. As a result, the brand has added almost 1,5 percentage point of market share during the period

This is a result of a growing line-up, filling all niches and creating new ones in the premium segment of the market. For example, the subcompact Audi A1 was introduced in 2009 and added almost 100.000 annual car sales to Audi’s total from 2011 onwards.

Then in 2010, Audi introduced the A7, a four door luxury coupe to compete with the Mercedes-Benz CLS. Both models showed similar sales in 2011 and 2012, but since 2013 the A7 has fallen behind the CLS in sales as Mercedes launched the Shooting Brake version. The introduction of the sporty top-of-the-line RS7 has been unable to reverse this slide.

In full-year 2020, global Audi sales were down 8.3% to 1,692,773 cars delivered 8worldwide. Despite weaker sales, Audi gained market share in most major markets. In China, by far the most important single-country market for Audi, sales increased by 5.4% to a record 727,358. The fourth quarter of 2020 was Audi’s best quarter ever with over half a million sales worldwide. Audi forecasts global sales growth in 2021.

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 :

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

Did Data Pre-Processing and Feature Engineering :

• Made dummies for improving my model's Performance.
• One-hot-Encoding was Implemented.
• 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
• Random Forest Regressor
• Extra Trees Regressor
• Decision Trees Regressor

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!

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

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