Experiment click to lead prediction with Azure ML I am experimenting now with the Azure ML Studio and I am trying to predict leads based on the clicks I have. 

I am exporting a data set of 60.000 Clicks and 8.000 Leads from these clicks. 

My data set:


With the "Edit Metadata" I have transformed all the features, except the hasLead in Categorical Feature.



I am testing as many combinations as I can with the data, but my "Recall" is very bad. Do you have some suggestions about what kind of missing data maybe can increase the Recall?

Here the evaluation of the model:
This looks like a two class boosted decision classifier.  That should perform quite well.  I used it several times about 2-3 months ago and got excellent results.  I'm guessing you are not training/testing the right features.  What is your target variable?  Is it 'hasLead'?  I'm not sure what will be statistically influential on that dependent variable, but you  can run a feature engineering exercise to see what independent variables have the most influence.  Run the code below; change to suit your specific needs (obviously, use your specific data).  

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# matplotlib inline

df = pd.read_csv("https://rodeo-tutorials.s3.amazonaws.com/data/credit-data-trainingset.csv")
df.head()

from sklearn.ensemble import RandomForestClassifier

features = np.array(['revolving_utilization_of_unsecured_lines',
                     'age', 'number_of_time30-59_days_past_due_not_worse',
                     'debt_ratio', 'monthly_income','number_of_open_credit_lines_and_loans', 
                     'number_of_times90_days_late', 'number_real_estate_loans_or_lines',
                     'number_of_time60-89_days_past_due_not_worse', 'number_of_dependents'])
clf = RandomForestClassifier()
clf.fit(df[features], df['serious_dlqin2yrs'])

# from the calculated importances, order them from most to least important
# and make a barplot so we can visualize what is/isn't important
importances = clf.feature_importances_
sorted_idx = np.argsort(importances)


padding = np.arange(len(features)) + 0.5
plt.barh(padding, importances[sorted_idx], align='center')
plt.yticks(padding, features[sorted_idx])
plt.xlabel("Relative Importance")
plt.title("Variable Importance")
plt.show()