/Customer_lifetime_value-prediction

In this project I have trained a ML model to predict lifetime value of customers and divided them into 3 categories :Gold ,Silver and Bronze. For better accuracy I have tune hyper parameters of multiple classification models and choose the best among them.

Primary LanguageJupyter Notebook

Customer_lifetime_value-prediction

About

In this project I have trained a ML model to predict lifetime value of customers and divided them into 3 categories :
Gold,
Silver
and Bronze.
For better accuracy I have tune hyper parameters of multiple classification models and choose the best among them. Elbow method was used for K-means clustering. Finally tuned version of xgboost model is used with 88.9% accuracy.

Languages Used

Python

Tools Used

  1. Google Colab
  2. Pandas
  3. Numpy
  4. Scikit-learn
  5. XGBoost
  6. Pickle
  7. RandomizedSearchCV
  8. Random Forest
  9. K means

Dataset Used

Online Retail Dataset from UCI Machine Repository was used. Link

Reference Resources

Kaggle Notebook