How to Speed up Model Training with Snapml | Geek Culture | HackerNoon

_#load data_Bank_Dataset=pd.read_csvPandas DataframeLet’s check the shape of the dataset in order to identify its size.The Bank Loan Status dataset has more than 100,000 rows of data and 19 columns. This dataset is large enough to evaluate the time difference when training a model with and without Snapml.You need to prepare the dataset by removing features that are not required, handling missing values and transforming all features into numerical values.#remove ID columnsBank_Dataset.

The code block below will firstly fill missing values in categorical columns by using the most frequent value in each categorical column. Then fill missing values in the numerical columns by using the average value of each numerical column. # fill missing values for categorical featuresBank_Dataset["Loan Status"].fillnaBank_Dataset["Term"].fillnaBank_Dataset["Years in current job"].fillnaBank_Dataset["Home Ownership"].fillnaBank_Dataset["Purpose"].fillna# fill missing values for integers featuresintergers_columns=list.columns)for column in intergers_columns:Bank_Dataset[column].

The first step in transformation is to use the LabelEncoder method from the scikit-learn library to preprocess two binary categorical columns . # preprocess binary categorical columnsle=LabelEncoderbinary_columns=["Loan Status", "Term"]for column in binary_columns:Bank_Dataset[column]=le.fit_transformfunction from the pandas library. This function will transform the following columns in the dataset.

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