prateekagr21 / Predicting-price-for-auction-cars

Doing the Prediction of the cars listed in auctions around the united states using various Machine Learning Algorithms.

Predicting-price-for-auction-cars

Predicting price of Auction listed cars around USA using Machine Learning Algorithms!

Get me a DeLorean
And then let's go back to the Future!

All over the U.S., you can find car dealer auctions open to the public. That means buying cars at auction prices is an option for just about anyone, even if you don't have a dealer's license. If you're interested in buying cars at wholesale prices, take a look at our car auction guide to find out how to buy the car you want at auction, just like a dealer.

Car dealer auctions are open only to those with a dealer license issued by the state. You can get your own dealer license if you wish, but the process can be long and difficult

Each state has its own set of requirements for car dealer licensing, usually with a set number of cars you must buy and sell each year to qualify.

Buying, modifying and reselling cars bought at car dealer auctions can turn you a tidy profit. But if you only plan on buying cars for yourself, it's probably best to buy from auctions that are open to the public instead.

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

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 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!

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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