Sample datafile is providing numerical and categorical variables for each case id.
We can calculate final score for each case id and see which case id is suitable for loan approval.
Approximately 10% of case ids is clearly suitable for loan.
# There are 22500 case ids with low propensity to loan approval
# There are 4500 case ids with medium propensity to loan approval
# There are 3000 case ids with high propensity to loan approval, suitable for loan
Provided metrics:
30 000 rows, 1 year data file, daily case id scores
Target:
There are 3 output variables: ap090, ct090 and PCTILE_SCORE
Methodologies used:
MEAN imputation
PYSPARK RANDOM FOREST ITERATIVE multivariate imputation
Ridge cross validation
Random Forest Regressor cross validation
Percentile score classification
# create connection
conn = sqlite3.connect("dataset.db")
cursor = conn.cursor()
# list 2 table names in connected database: dataset and metadata
x=cursor.execute("SELECT distinct name FROM sqlite_master where type='table'")
for y in x.fetchall():
print(y)
# metadata - display variables description in data frame
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