This lecture talks simply talks about Linear Regression. The lecture also shows how to get the job done on Python and with the help of sklearn.

To predict the cereal ratings of the columns that give ingredients from the given dataset using linear regression with sklearn

Speeding up a sklearn model pipeline to serve single predictions with very low latency. Writing your own sklearn functions, (for now final)

Combining tree based models with a linear baseline model to improve extrapolation. Writing your own sklearn functions.

Applying Machine Learning To The E.Coli Class Imbalance Dataset. The E.Coli dataset is a very popular dataset to experiment on because it is a multi-classification that has several imbalances

How to use Pandas, Sklearn and functions. This article identifies the most common mistakes prospect data scientists make and discuss how to avoid them. Without further ado, let’s jump straight into it.

Email Classification works on the same basic concepts. By going through the text of the email, we will use Machine Learning algorithms to predict whether the email has been written by one person or the other. Implementing Machine Learning Algorithms to Classify Emails. The best Machine Learning algorithm for Email Classification

Apply Machine Learning on a Cancer Dataset. In this article, take a look at how to apply machine learning on a cancer dataset.

Machine learning basic library abstract. Before starting machine learning i’m starting machine learning required library using python.

I will share some popular machine learning algorithms to predict the housing prices and the live model that I have built. My objective is to find a model that can generate a high accuracy of the housing prices, based on the available dataset, such that given a new property and with the required information, we will know whether the property is over or under-valued.

We will also see by how using the simple machine learning models like KNeighborsClassifier and LogisticRegression we can reduce overfitting and help our model generalize better on unseen data even with a less amount of training data that we have.

Enrich your train fold with a custom sampler inside an imblearn pipeline. I wouldn’t be able to write this article without the help of my colleagues and people from StackOverflow!

We discussed that the metric that we will be basing our results on was F1 Score using the Confusion Matrix. This blog will discuss, in depth, why.

Classifying drum samples with logistic regression using a large number of features generated using tsfresh. You can also find this on GitHub. This GitHub repository includes everything you need to run the analyses yourself.

Welcome back! In my previous post I wrote an EDA (Exploratory Data Analysis) on Titanic Survival dataset. Check it out now if you haven’t already. Anyway, in this article I would like to be more focusing on how to create a machine learning model which is able to predict whether a Titanic passenger survived based on their attributes i.e. gender, title, age and many more.

No spoilers here ;-)A long time ago I asked on Twitter if someone could help me with a puzzling problem. A tool I was using utilized the scipy linear algebra package to perform the calculations. Most of the time was spent, running the pinv function, which makes calculates the inverse matrix. There are four functions in the scipy.linalg module, that can calculate the inverse matrix: pinv, pinv2, pinvh, and inv.

Find out whether the person has heart disease or not in two ways. Well,well guys..Heart Disease Classification using structured data.

Imputing Missing Data Using Sklearn SimpleImputer. In this post, learn how to use Python's Sklearn SimpleImputer for imputing/replacing numerical and categorical missing data using different strategies.

It is easy for us to visualize two or three dimensional data, but once it goes beyond three dimensions, it becomes much harder to see what high dimensional data looks like.

TF-IDF is a simple twist in the bag of words approach. Bag of words just means (# times word w appears in a document d). TF-IDF stands for term frequency times inverse document frequency.