ANNImplementataion_Keras

A simple Implementation of Artificial Neural Networks in Keras, We used a case study of an online store which sells audiobooks. They are looking for the model which is able to predict that out customers that have bought the books previously which customers will buy again and which of them will not. So they spend there add budget to only those customers which have chances to buy again.

You were given data from an audio book app. Logically it relates to the audio version of books. Only each customer in the database has made a purchase at least once. That's the condition to be included. We want to create a machine learning algorithm based on our data that can predict if a customer will buy again from the audio book company. The main idea is that the company shouldn't spend its advertising budget targeting individuals who are unlikely to come back if we can focus our efforts on customers likely to convert again. We can obtain improved sales and profitability figures. So, our model will take several metrics and we'll try to predict human behavior. A side effect of our study is that the model will show us which are the most important metrics for a customer to come back having the data and the technology to identify prospective customers creates a lot of value and growth opportunities. It is one of the better applications of ANN. We will have a csv file named ‘Audiobooks_data.csv’ (The file on which we will apply will not include the first row which contains the column names). Let's go through the columns and see why each one of them could be of use.

• First, we have a customer ID here. ID is like a name whether the ID is 1 2 3 it makes no difference as no information is contained in the ID. We will skip it in our algorithm.

• Next, we have book length the overall book length is the sum of the lengths of all purchases.

• We also have the average book length. The average book length is basically the sum divided by the number of purchases. So, somebody has bought a single audio book. The average length and the overall length for this person will be equal alright. There is no need to include the number of purchases as it is contained in the two variables we just described.

• Then we have the overall price paid and the average price paid. These variables were constructed in the same way as those for book length. The prices in dollars. Although it makes no difference to the algorithm. By the way the price variable is almost always a good predictor of behavior.

• The next variable is review; review is a Boolean. It shows if the customer left the review. This is a metric that shows engagement with the platform. Our assumption is that people will leave reviews are more likely to convert again.

• Then we have review out of 10 this is a different variable. It measures the review of a customer on a scale from 1 to 10. Logically we will only have a value for people who left a review by examining the table. We quickly see most people leave no review. As in most marketplaces that's bad for our dataset and bad in general. We have decided to leave the reviews posted to the platform and substitute all missing values with the average review. The average is 8.91 for our machine learning algorithm 8.91 would mean the status quo. A review bigger than 8.91 would indicate above average feelings while the review less than 8.91 would indicate below average feelings. Review is yet another variable that is an average. A customer may have bought two or three books on the platform. The average review he/she left indicates his/her feelings towards the content on the medium or better the medium as a whole. An average of two out of ten indicates the person did not have a pleasant experience with audio books especially when the average is 8.91. It is logical that such a customer is not likely to buy again.

• Then we have total minutes listened which is a measure of engagement.

• Next to it we have completion; completion is the total minutes listened divided by the total length of books a person has purchased. Assuming people don't relisten to books both variables (both variables mean: Minutes listened and completion) are self-explanatory.

• The next variable is support requests. It is numerical and shows the total number of support requests the person has opened. Support is anything from Forgotten Password to assistance on using the platform once more, this is a measure of engagement. It may turn out that the more support a person needed the more he or she got fed up with the platform and abandoned it or he or she likes it so much that by using it stumbles upon different issues. Unlike someone who never opens the app.

• Finally, we have a variable measuring the difference between the last time a person interacted with the platform and their first purchase date. That's yet another measure of engagement. The bigger the difference the better. If a person engages regularly with the platform this difference will be bigger. Thus, the customer is likely to convert again but if the value of this variable is zero we are sure the customer has never access what he has bought. Or perhaps he did it on the first day of purchase only. So, it is unlikely he or she will convert again.

All right. These are our inputs. It is always necessary to ask how the data was gathered this piece of information is valuable for any analysis. The data was gathered from the audio book app. As we said it represents two years’ worth of engagement. Now we are doing supervised learning. So, we need targets right. The targets will be a Boolean one. 1 if a person is converted and 0 if he/she didn’t. But what does it mean to convert. That's the big question here. We have taken an extra six months of data after the two-year period to check if a user converted. So, we took two years and six months of data the first two years are contained in the data set you have here the next 6 months will show us if a person converted or not. In other words, if he or she bought another book and if that happened we can count them as a conversion and the target will be 1. Otherwise it is 0. That's how we created the targets column 6 months sounded reasonable enough for us. If one buys no new audio book in that period chances are they've gone to a competitor or didn't like the audio book. So, our task is simple create a machine learning algorithm that can predict if a customer will buy again. This is a classification problem with two classes won't buy and will buy represented by zeros and ones.