mtahir19/Predicting-the-price-of-a-part

Predicting-the-price-of-a-part

Outlook:

I enjoyed learning a lot of new business questions. There are a lot of interesting things to explore further if I have more discussion and time. I got highest score with polynomial regression and random forest as compared to linear regression, SVM, ridge and lasso.

I want to try piple-line using decision tree, lasso and elasticnet, or XGBoost etc. Also I need to do feature engineering (interaction, groupby etc) and parameter tuning for further optimization.

I am going to drop around 244 observations because they are structured missing in most columns.

Data is clearly small and our business interests need clarity like what we want to achieve; either metric score of which field (feature or column) is usually more important.

--- The work is done as following:

Part-1: Imports and Data Extraction

Part-2: Exploratory Data Analysis

Part-3: Model Prediction (model-1: linear regression, model-2: polynomial regression, model-3: ridge, model-4: lassocv, model-5: random forest, model-6: support vector machine)

Part-4: Deployment; Model work for testing