Basic ML prediction example

Here’s a basic example of doing predictions using machine learning in Peliqan, using a low-code Python script. We are predicting if certain invoices will get paid (field “status”), based on historical data:

from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor

# Load a table from Peliqan
dbconn = pq.dbconnect(pq.DW_NAME)
df_full = dbconn.fetch(pq.DW_NAME, 'accounting', 'invoices', df=True)

# Select columns for prediction 
df = df_full[["first_name","product_id","qty", "amount", "status"]]

# Input columns to make predictions (remove predict column and first name)
X = df.drop(['first_name', 'status'], axis=1)

# Column to predict
Y = df['status']

# Split the data in a training data set, and a test data set
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.20, random_state=0)

# Train the model on historical data (training data set)
model = DecisionTreeRegressor(max_depth=5).fit(X_train,Y_train)

# Make prediction and add as new column
# We are using our test dataset but we could also apply this on new invoices
X_test["prediction"] = model.predict(X_test)

# Merge result with original list to include invoice name etc.
df_final = X_test.merge(df)

# Show result using Streamlit
st.dataframe(df_final[["first_name","prediction"]], use_container_width=True)