/Used_Car_Price_Prediction

Evaluate factors affecting the price second-hand cars, Predict the Price by their model, year-launched, engine volume etc.

Primary LanguageJupyter Notebook

Used_Car_Price_Prediction

Data Visualisation

Data Cleaning

Feature Selection

Model


* Feature Selection methods: Spearman's Corelation coefficeint and ANOVA
* Model : Decision Tree Regressor and RandomForest Regressor , with max_depth 7.
* Accuracy in both models is coming approximately 60%.

pandas = 1.1.4
numpy = 1.18.5
matplotlib = 3.3.2
seaborn = 0.11.0
sklearn = 0.23.2
statsmodels = 0.12.1