This is my customer segmentation project

• Reading the file using pandas
• checking the columns data types using describe function
• checking for null values
• checking for duplicate records and drop the duplicated values

Univariate analysis

• perform the univariate analysis i.e. how the each feature is distributed
• if any value treaten as null value replace it with mode of perticular feature

Outlier detection

• Detect the outliers in categorical columns using box plot
• impute te IQR values
• replace the outliers with IQR values

Bivariate analysis

• Bivariate analysis is knowing how feature is distrubuted with respect totarget variable

Encoding

• Transforming categorical features into numerical values

Feature selection

• checking for the features for correlated or not by setting threshold value

splitting the data

• splitting the data into train data and test data

Balencing the data

• Here the data is imbalenced balencing the train data using oversampling technique called SMOTTEN

standerdization

• Transforming train data from range of numerical values into between 0 to

Model building

• Building the model and train with different parameters and find the best parameters using gridserchCV
• check the performance parameters and check the model accuracy using AURROC metric
• find the best model with high accuracy and high AOUROC value