AI News, Machine Learning for Retail Price Recommendation with Python

Machine Learning for Retail Price Recommendation with Python

It is obvious that the average price is higher when buyer pays shipping.

There are 1265 unique values in category name column Top 10 most common category names: There seems to be various on the average price between each item condition id.

Under the umbrella of the DMTK project of Microsoft, LightGBM is a gradient boosting framework that uses tree based learning algorithms.

Helper function for LightGBM: Drop rows where price = 0 Merge train and new test data.

The Stanford Classifier

The Stanford Classifier is a general purpose classifier - something that takes a set of input data and assigns each of them to one of a set of categories.

(This is referred to as 'supervised learning'.) The classifier can work with (scaled) real-valued and categorical inputs, and supports several machine learning algorithms.

While you can specify most options on the command line, normally the easiest way to train and test models with the Stanford Classifier is through use of properties files that record all the options used.

In the top level folder of the Stanford Classifier, the following command will build a model for this data set and test it on the test data set in the simplest possible way:

The next part then shows the results of testing the model on a separate test set of data, and the final 5 lines give the test results:

For each class, the results show the number of true positives, false negatives, false positives, and true negatives, the class accuracy, precision, recall and F1 measure.

It then gives a summary F1 over the whole data set, either micro-averaged (each test item counts equally) or macro-averaged (each class counts equally).

Mostly the system is using character n-grams - short subsequences of characters - though it also has a couple of other features that include a class frequency prior and a feature for the bucketed length of the name.

Also, often it is useful to mix a properties file and some command-line flags: if running a series of experiments, you might have the baseline classifier configuration in a properties file but put differences in properties for a series of experiments on the command-line.

(This form of output is especially easily interpretable for categorical features.) You see that most of the clearest, best features are particular character n-grams that indicate disease words, such as: ia$, ma$, sis (where $ indicates the end of string).

For that feature, the weight for class 2 (disease) is 1.0975 - this is a strong positive vote for this feature indicating a disease not a cheese.

In the download, there is a version of the 150 item data set divided into 130 training examples and 20 test examples, and a properties file suitable for training a classifier from it.

The number of examples in each class is roughly balanced, so there is presumably little value in the useClassFeature property which puts in a feature that models the overall distribution of classes.

You can instead, delete features for columns 2 and 4 and just use the sepal and petal lengths rather than also widths, and also still get 100% accuracy on our test set.

Preparing and Cleaning Data for Machine Learning

In this blog post, Dataquest student Daniel Osei takes us through examining a dataset, selecting columns for features, exploring the data visually and then encoding the features for machine learning.

Approved loans are listed on the Lending Club website, where qualified investors can browse recently approved loans, the borrower's credit score, the purpose for the loan, and other information from the application.

To ensure that code run fast for us, we need to reduce the size of lending_club_loans.csv by doing the following: We'll also name the filtered dataset loans_2007 and later at the end of this section save it as loans_2007.csv to keep it separate from the raw data.

Now, let's go ahead and perform these steps: Let's use the pandas head() method to display first three rows of the loans_2007 DataFrame, just to make sure we were able to load the dataset properly: Let's also use pandas .shape attribute to view the number of samples and features we're dealing with at this stage: It's a great idea to spend some time to familiarize ourselves with the columns in the dataset, to understand what each feature represents.

Now that we've got the data dictionary loaded, let's join the first row of loans_2007 to the data_dictionary DataFrame to give us a preview DataFrame with the following columns: When we printed the shape of loans_2007 earlier, we noticed that it had 56 columns which also means this preview DataFrame has 56 rows.

As you explore the features to better understand each of them, you'll want to pay attention to any column that: I'll say it again to emphasize it because it's important: We need to especially pay close attention to data leakage, which can cause the model to overfit.

Let's display the first 19 rows of preview and analyze them: After analyzing the columns, we can conclude that the following features can be removed: Lending Club uses a borrower's grade and payment term (30 or months) to assign an interest rate (you can read more about Rates &

And, that's exactly what grading does - it segments borrowers based on their credit score and other behaviors, which is we should keep the grade column and drop interest int_rate and sub_grade.

We can drop the following columns: Let's go ahead and remove these 5 columns from the DataFrame: Let's analyze the last group of features: In this last group of columns, we need to drop the following, all of which leak data from the future: Let's drop our last group of columns: Now, besides the explanations provided here in the Description column,let's learn more about fico_range_low, fico_range_high, last_fico_range_low, and last_fico_range_high.

When a borrower applies for a loan, Lending Club gets the borrowers credit score from FICO - they are given a lower and upper limit of the range that the borrowers score belongs to, and they store those values as fico_range_low, fico_range_high.

In the report for the project, the group listed the current credit score (last_fico_range) among late fees and recovery fees as fields they mistakenly added to the features but state that they later learned these columns all leak information into the future.

This blog examines in-depth the FICO scores for lending club loans, and notes that while looking at the trend of the FICO scores is a great predictor of whether a loan will default, that because FICO scores continue to be updated by the Lending Club after a loan is funded, a defaulting loan can lower the borrowers score, or in other words, will leak data.

Lets take a look at the values in these columns: Let's get rid of the missing values, then plot histograms to look at the ranges of the two columns: Let's now go ahead and create a column for the average of fico_range_low and fico_range_high columns and name it fico_average.

Now, let's decide on the appropriate column to use as a target column for modeling - keep in mind the main goal is predict who will pay off a loan and who will default.

I have pulled that data together in a table below so we can see the unique values, their frequency in the dataset and what each means: Remember, our goal is to build a machine learning model that can learn from past loans in trying to predict which loans will be paid off and which won't.

These plots indicate that a significant number of borrowers in our dataset paid off their loan - 85.62% of loan borrowers paid off amount borrowed, while 14.38% unfortunately defaulted.

We need to handle missing values and categorical features before feeding the data into a machine learning algorithm, because the mathematics underlying most machine learning models assumes that the data is numerical and contains no missing values.

To reinforce this requirement, scikit-learn will return an error if you try to train a model using data that contain missing values or non-numeric values when working with models like linear regression and logistic regression.

We can return the number of missing values across the DataFrame by: Notice while most of the columns have 0 missing values, title has 9 missing values, revol_util has 48, and pub_rec_bankruptcies contains 675 rows with missing values.

In addition, we'll remove the remaining rows containing null values, which means we'll lose a bit of data, but in return keep some extra features to use for prediction.

This means that we'll keep the title and revol_util columns, just removing rows containing missing values, but drop the pub_rec_bankruptcies column entirely since more than 1% of the rows have a missing value for this column.

We learned from the description of columns in the preview DataFrame earlier that revol_util is a revolving line utilization rate or the amount of credit the borrower is using relative to all available credit (read more here).

Lastly, notice the first row's values for both earliest_cr_line and last_credit_pull_d columns contain date values that would require a good amount of feature engineering for them to be potentially useful: We'll remove these date columns from the DataFrame.

First, let's explore the unique value counts of the six columns that seem like they contain categorical values Most of these coumns contain discrete categorical values which we can encode as dummy variables and keep.

It appears the purpose and title columns do contain overlapping information, but the purpose column contains fewer discrete values and is cleaner, so we'll keep it and drop title.

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