The project uses the pandas, numpy, matplotlib, seaborn, scikit-learn, and tensorflow libraries. The dataset used in the project is an Excel file (merc.xlsx) containing the characteristics of Mercedes-Benz cars.
The code for the project is written in Jupyter Notebook, and comments have been added to make the code understandable.
To analyze the dataset, the head(), info(), describe(), and isnull().sum() functions are used. Additionally, the hist(), distplot(), bar(), and heatmap() functions are used to visualize the data distribution.
To handle outliers in the dataset, the sort_values() and iloc[] functions are used. Additionally, the data from 1970 is removed, and the transmission column is dropped from the dataset.
To prepare the dataset for the machine learning model, the MinMaxScaler() function is used. Then, the dataset is split into training and test data.
To create the model, the Sequential() and Dense() functions are used. The model is trained using Adam optimization and mean squared error (mse) loss.
In conclusion, the project demonstrates the application of machine learning methods using an artificial neural network to predict the market prices of a car seller's cars.