Credit card validator python project code#
Credit card validator python project free#

Step 1: Import relevant libraries: Import all the relevant python libraries for building supervised machine learning algorithms. So let's restart the session, clear the cache and start afresh! try: from IPython import get_ipython get_ipython().magic('clear') get_ipython().magic('reset -f') except: pass Step 0: Restart the session: It’s a good practice to restart the session and to remove all the temporary variables from the interactive development environment before we start coding. Understanding the end-to-end structure of your dataset and reshaping the variables is the gateway to a qualitative predictive modelling initiative. “Your model is only as good as your data” But like in life, wisdom kicks in at a later stage! After witnessing the real-world Machine Learning business challenges, I can’t stress the importance of Data preprocessing and Data Evaluation.Īlways remember the following golden rule in predictive analytics: If you had asked the 20-year-old me, I would have jumped straight into model selection as its coolest thing to do in machine learning. Let’s look into each one of these aforesaid steps in detail here below Section A: Data Preprocessing

Credit card validator python project free#

Feel free to ask me if you have any questions as you work along.

Credit card validator python project code#

To understand the business challenge and the proposed solution, I would recommend you to download the dataset and to code with me.

In real-world, we need to go through seven major stages to successfully predict customer churn: Given that we have data on current and prior customer transactions in the telecom dataset, this is a standardized supervised classification problem that tries to predict a binary outcome (Y/N).īy the end of this article, let’s attempt to solve some of the key business challenges pertaining to customer attrition like say, (1) what is the likelihood of an active customer leaving an organization? (2) what are key indicators of a customer churn? (3) what retention strategies can be implemented based on the results to diminish prospective customer churn? A supervised machine learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. Supervised Machine Learning is nothing but learning a function that maps an input to an output based on example input-output pairs. Let’s make use of a customer transaction dataset from Kaggle to understand the key steps involved in predicting customer attrition in Python. If we could figure out why a customer leaves and when they leave with reasonable accuracy, it would immensely help the organization to strategize their retention initiatives manifold. Once we run the above code snippet, we will see: Maximum Accuracy 1.0Ĭlearly, the model performace in quite high in any case across 10 fold stratified cross validation.Customer attrition ( a.k.a customer churn) is one of the biggest expenditures of any organization. Here we have maximum accuracy, minimum accuracy and average accuracy across 10 fold validation set. Print('Overall Accuracy:',mean(lst_accu_stratified)) Print('Minimum Accuracy:',min(lst_accu_stratified)) Print('Maximum Accuracy',max(lst_accu_stratified)) We have then fit our model at each set and thereby calculated accuracy score. Skf.split has divided our model into 10 random index set. Lst_accu_stratified.append(model.score(X_test_fold, y_test_fold)) Step 4 - Building Stratified K fold cross validationįor train_index, test_index in skf.split(X, y): Now for StratifiedKFold, we have kept n_splits to be 10, dividing our dataset for 10 times.

We have simply built a regressor model with LogisticRegression with default values. Skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=1) Step 3 - Building the model and Cross Validation model Here, we have used load_breast_cancer function to import our dataset in two list form (X and y) and therefore kept return_X_y to be True. Now StratifiedKFold will help us in performing Stratified K fold cross-validation. Also, we have exported LogisticRegression to build the model. Here sklearn.dataset is used to import one classification based model dataset. Step 4 - Building Stratified K fold cross validationįrom sklearn.datasets import load_breast_cancerįrom sklearn.linear_model import LogisticRegressionįrom sklearn.model_selection import StratifiedKFold.

Step 3 - Building the model and Cross Validation model.