Google Analytics Customer Revenue Prediction

Predict how much GStore customers will spend .

Overview

The 80/20 rule has proven true for many businesses–only a small percentage of customers produce most of the revenue. As such, marketing teams are challenged to make appropriate investments in promotional strategies.

The challenge is to analyze a Google Merchandise Store (also known as GStore, where Google swag is sold) customer dataset to predict revenue per customer. Hopefully, the outcome will be more actionable operational changes and a better use of marketing budgets for those companies who choose to use data analysis on top of GA data.

Data

The dataset is available here

Installation

If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. This project requires the following Python libraries installed:

• sci-kit learn

conda install scikit-learn

• lightgbm

conda install lightgbm

• xgboost

conda install xgboost

• catboost

conda install catboost

• Bayes Optimization

pip install bayesian-optimization

Code

• Note: since iterative process of preproceeing taken, the cell numbers might not be in order, but the code cells are in order

+ Full code is available in customer_revenue_prediction.ipynb notebook.

Download the data from the above given link. Install Anaconda and necessary packages.

The following steps are taken to analyse and apply Machine Learning model

Preprocessing

• Some columns namely device,geoNetworkDomain,traficSource,totals in the dataset are in JSON form. We need to convert this JSON columns to tabular format. This is done using ajson_normalize module.
• Created subcolumns for each JSON columns.
• check number of unique values in each column and drop constant columns. This is done using

pd.nunique(dropna=False) 

• Explore the target variable i.e, totals.transactionRevenue

Feature Engineering

• Created new columns (_day,_weekday,_month,_year) from date feature.
• created _visitHour feature from the visitStartTime which is given in timestamp format.

Exploratory Data Analysis

Date and Time v/s_transactionRevenue_

• visalised the change of transactionRevenue against _day,_weekday,_month,_year.
• visualised the change of transactionRevenue against _visitHour.

device columns v/s_transactionRevenue_

• visualised the change of transactionRevenue against device.browser, device.deviceCategory, device.operatingSystem.

geoNetwork v/s transactionRevenue

• visualised the change of transactionRevenue against geoNetwork.continent, geoNetwork.country, geoNetwork.subContinent , geoNetwork.networkDomain.

trafficSource v/s transactionRevenue

• visualised the change oftransactionRevenue against trafficSource.source,trafficSource.referralPath', trafficSource.medium`.

totals v/s transactionRevenue

• visualised the change oftransactionRevenue against totals.pageViews, totals.hits.

Missing value treatment

pd.fillna(value)

• Numerical columns In numerical columns list only totals.hits,totals.bounces and totals.pageViews were missing, so filled it with appropriate value.

• Categorical columns some columns had more than 60% of missing value so not able to fill it with existing category. Hence filled it with a new category 'unknown'.

Label Encoding

All categorical columns are encoded using LabelEncoder() class which is imported from sklearn.preprocessing module.

Train and Validation split

• train_v2.csv dataset has covered the data from 1st August 2016 to 30th April 2018.
• Last 4 months data i.e, 1st Jan 2018 to 30th April 2018 as validation set.

Training

• I have used the LightGBM model.
• It supports parallel and GPU learning.
• It is highly efficeint in handling large size data.
• To enable GPU access we need to install some drivers
• for more information check here

Hyper parameter tuning

This is the most important step in the modelling to boost the model performace. It is a time consuming process Bayesian Optimization is used for tuning.

• well known tuning methods like GridSeachCV and RandomizedSearchCV doesn't keep track of previous error, so not a better choice for perfect tuning.
• Bayesian Optimization tends to find good points in search space with relatively few function evaluations.
• selected some important parameters of LightGBM for tuning, because to tune all parameters it takes lot of time.

Cross Validation

• Cross Validation is used to assess the predictive performance of the models and and to judge how they perform outside the sample to a new data set also known as test data
• Used n_folds = 5

Evaluation of a Validation set

• find the root mean square error(rmse) of actual transactionRevenue and predicted transactionRevenue.
• check whether model is optimised or not.

Ensembling

• I tried to ensemble LightGBM, xgboost, catboost but performance did not improve much(lack of time to tune paremeters of xgboost and catboost).

Submission

• The predicted transactionRevenue of test set will be in np.log1p form, so take inverse i,e apply np.expm1.
• calculate total transactionRevenue of each visitor.

import pandas as pd
sub_df = sub_df.groupby('fullVisitorID')['transactionRevenue'].sum().reset_index()

• apply np.log1p to the transactionRevenue Series.
• change the column name of transactionRevenue to PredictedLogRevenue
• save the dataframe to csv format using

sub_df.to_csv(path, index=False)

Public Leaderboard score

My best score on kaggle for this competition is

root mean square error 1.7061