Category Archives: Machine Learning

Predicting Customer Churn with Machine Learning

Customer churn prediction using machine learning

Customer churn, also known as customer attrition, is a major problem for businesses that rely on recurring revenue. Customer churn costs businesses billions of dollars every year, and it’s only getting worse as customers become more and more fickle. In fact, it’s been estimated that the average company loses 10-15% of its customers each year. That number may seem small, but it can have a huge impact on a company’s bottom line. Fortunately, there’s a way to combat churn: by using machine learning to predict which customers are likely to churn. In this blog post, we’ll discuss how customer churn prediction works and why it’s so important. We’ll also provide …

Continue reading

Posted in AI, Data Science, Machine Learning. Tagged with , , .

Stacking Classifier Sklearn Python Example

Stacking classifier python example

In this blog post, we will be going over a very simple example of how to train a stacking classifier machine learning model in Python using the Sklearn library and learn the concepts of stacking classifier. A stacking classifier is an ensemble learning method that combines multiple classification models to create one “super” model. This can often lead to improved performance, since the combined model can learn from the strengths of each individual model. What are Stacking Classifiers? Stacking is a machine learning ensemble technique that combines multiple models to form a single powerful model. The individual models are trained on different subsets of the data using some type of …

Continue reading

Posted in Data Science, Machine Learning, Python. Tagged with , , .

Decision Tree Hyperparameter Tuning Grid Search Example

decision tree grid search hyperparameter tuning example

The output prints out grid search across different values of hyperparameters, the model score with best hyperparameters and the most optimal hyperparameters value. In the above code, the decision tree model is train and evaluate our for each value combination and choose the combination that results in the best performance. In this case, “best performance” could be defined as either accuracy or AUC (area under the curve). Once we’ve found the best performing combination of hyperparameters, we can then train our final model using those values and deploy it to production. Conclusion In this blog post, we explored how to use grid search to tune the hyperparameters of a Decision …

Continue reading

Posted in Data Science, Machine Learning, Python. Tagged with , , .

Reinforcement Learning Real-world examples

Reinforcement-learning-real-world-example

 In this blog post, we’ll learn about some real-world / real-life examples of Reinforcement learning, one of the different approaches to machine learning where other approaches are supervised and unsupervised learning. Reinforcement learning is a type of machine learning that enables a computer system to learn how to make choices by being rewarded for its successes. This can be an extremely powerful tool for optimization and decision-making. It’s one of the most popular machine learning methods used today. Before looking into the real-world examples of Reinforcement learning, let’s quickly understand what is reinforcement learning. Introduction to Reinforcement Learning (RL) Reinforcement learning is an approach to machine learning in which the agents …

Continue reading

Posted in Data Science, Machine Learning. Tagged with , .

Generalized Linear Models Explained with Examples

Generalized linear models (GLMs) are a powerful tool for data scientists, providing a flexible way to model data. In this post, you will learn about the concepts of generalized linear models (GLM) with the help of Python examples.  It is very important for data scientists to understand the concepts of generalized linear models and how are they different from general linear models such as regression or ANOVA models.  What are Generalized Linear Models? Generalized linear models (GLM) are a type of statistical models that can be used to model data that is not normally distributed. It is a flexible general framework that can be used to build many types of regression models, including …

Continue reading

Posted in Data Science, Machine Learning, Python. Tagged with , .

Import or Upload Local File to Google Colab

How to read CSV file in Google Colab

Google Colab is a powerful tool that allows you to run Python code in the cloud. This can be useful for a variety of tasks, including data analysis and machine learning. One of the lesser known features of Google Colab is that you can also import or upload files stored on your local drive. In this article, we will show you how to read a file from your local drive in Google Colab using a quick code sample. There are a few reasons why you as a data scientist might need to learn how to read files from your local drive in Google Colab. One reason is that you may …

Continue reading

Posted in AI, Data Science, Machine Learning, Python. Tagged with , , .

Ridge Classification Concepts & Python Examples

Ridge classifier python example

In machine learning, ridge classification is a technique used to analyze linear discriminant models. It is a form of regularization that penalizes model coefficients to prevent overfitting. Overfitting is a common issue in machine learning that occurs when a model is too complex and captures noise in the data instead of the underlying signal. This can lead to poor generalization performance on new data. Ridge classification addresses this problem by adding a penalty term to the cost function that discourage complexity. This results in a model that is better able to generalize to new data. In this post, you will learn about Ridge classifier in detail with the help of …

Continue reading

Posted in Data Science, Machine Learning, Python. Tagged with , , .

PCA vs LDA Differences, Plots, Examples

PCA plot for IRIS dataset

Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two of the most popular dimensionality reduction techniques. Both methods are used to reduce the number of features in a dataset while retaining as much information as possible. But how do they differ, and when should you use one method over the other? As data scientists, it is important to get a good understanding around this concept as it is used in building machine learning models. Keep reading to find out with the help of Python code & examples. How does PCA work? Principal Component Analysis (PCA) works by identifying the directions (components) that maximize the variance in a dataset. …

Continue reading

Posted in Data Science, Machine Learning, Python. Tagged with , , .

How to Create Pandas Dataframe from Numpy Array

Scatterplot of Datafrae columns

Pandas is a library for data analysis in Python. It offers a wide range of features, including working with missing data, handling time series data, and reading and writing data in different formats. Pandas also provides an efficient way to manipulate and calculate data. One of its key features is the Pandas DataFrame, which is a two-dimensional array with labeled rows and columns. A DataFrame is a table-like structure that contains columns and rows of data. Creating a Pandas DataFrame from a NumPy array is simple. In this post, you will get a code sample for creating a Pandas Dataframe using a Numpy array with Python programming. Step 1: Load …

Continue reading

Posted in AI, Data Science, Machine Learning. Tagged with , , .

Ensemble Methods in Machine Learning: Examples

voting ensemble method

Machine learning models are often trained with a variety of different methods in order to create a more accurate prediction. Ensemble methods are one way to do this, and involve combining the predictions of several different models in order to get a more accurate result. When different models make predictions together, it can help create a more accurate result. Data scientists should care about this because it can help them create models that are more accurate. In this article, we will look at some of the common ensemble methods used in machine learning. Data scientists should care about this because it can help them create models that are more accurate. …

Continue reading

Posted in Data analytics, Data Science, Machine Learning. Tagged with , .

Learning Curves Python Sklearn Example

Learning curve explained with python example

In this post, you will learn about how to use learning curves using Python code (Sklearn) example to determine machine learning model bias-variance. Knowing how to use learning curves will help you assess/diagnose whether the model is suffering from high bias (underfitting) or high variance (overfitting) and whether increasing training data samples could help solve the bias or variance problem. You may want to check some of the following posts in order to get a better understanding of bias-variance and underfitting-overfitting. Bias-variance concepts and interview questions Overfitting/Underfitting concepts and interview questions What are learning curves & why they are important? Learning curve in machine learning is used to assess how models will …

Continue reading

Posted in Data Science, Machine Learning, Python. Tagged with , , , .

Machine Learning Sklearn Pipeline – Python Example

Machine-learning-pipeline-Sklearn

In this post, you will learning about concepts about machine learning (ML) pipeline and how to build ML pipeline using Python Sklearn Pipeline (sklearn.pipeline) package. Getting to know how to use Sklearn.pipeline effectively for training/testing machine learning models will help automate various different activities such as feature scaling, feature selection / extraction and training/testing the models. It is recommended for data scientists (Python) to get a good understanding of Sklearn.pipeline.  Introduction to Machine Learning Pipeline & Sklearn.pipeline Machine Learning (ML) pipeline, theoretically, represents different steps including data transformation and prediction through which data passes. The outcome of the pipeline is the trained model which can be used for making the predictions. …

Continue reading

Posted in Data Science, Machine Learning, Python. Tagged with , , , .

Sequence Models Quiz 1 – Test Your Understanding

interview questions for machine learning

Sequence modeling is extremely important for data scientists as it can be used in a variety of real-world applications. Sequence modeling is used in speech recognition, image recognition, machine translation, and text summarization. These are all important applications that data scientists must be familiar with. As a data scientist, it is important to have a good understanding of sequence modeling and how it can be used to solve real-world problems. In this blog, we’ll be looking at a quiz around sequence models, more specifically the different types of sequence models. This will help us understand how sequence models work and can be used in an interview situation. Before getting into …

Continue reading

Posted in Career Planning, Data Science, Interview questions, Machine Learning.

Credit Risk Modeling & Machine Learning Use Cases

credit risk modeling and machine learning use cases

Credit risk modeling is a process of estimating the probability that a borrower will default on their loan. This is done by analyzing historical data about borrowers’ credit behavior. Credit risk models are used by banks and financial institutions to make better decisions about who to lend money to, how much to extend, and when to pull back. Banks and financial institutions are under constant pressure to improve their business outcomes. One way they are doing this is by using machine learning to better predict credit risk. By understanding the factors that contribute to a borrower’s likelihood of default, banks can make more informed decisions about who to lend money …

Continue reading

Posted in Data Science, Machine Learning. Tagged with , .

Performance metrics for Time-series Forecasting models

time-series forecasting model performance metrics

Time-series forecasting is a specific type of forecasting / predictive modeling that uses historical data to predict future trends in a particular time series. There are several different metrics that can be used to measure the accuracy and efficacy of a time-series forecasting model, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and others. By understanding these performance metrics, you can better assess the effectiveness of your time-series forecasting model and make necessary adjustments as needed. In this blog, you will learn about the different time-series forecasting model performance metrics and how to use them for model evaluation. Check out a related post – Different types of time-series …

Continue reading

Posted in Data Science, Machine Learning. Tagged with .

Sample Dataset for Regression & Classification: Python

Sample-data-set-plot-for-regression

A lot of beginners in the field of data science / machine learning are intimidated by the prospect of doing data analysis and building regression (linear) & classification models in Python. But with an ability to create sample dataset using Python packages, you can practice your skills and build your confidence over a period of time. The technique demonstrated in this blog post to create and visualize / plot the sample dataset includes datasets that can be used for regression models such as linear regression and classification models such as logistic regression, random forest, SVM etc. You can use this technique to explore different methods for solving the same problem. …

Continue reading

Posted in Data Science, Machine Learning, Python. Tagged with , , .