Category: Machine Learning
What do you understand by true positive rate and false-positive rate?
True Positive rate (TRP) is the ratio of True Positives to True Positives and False Negatives. It is the probability that an actual positive will test as positive. TPR = TP / TP + FN The False Positive Rate (FPR) is the ratio of the False Positives to all the positives (True positives and false…
Read More3 Concepts Every Data Scientist Must Know Part – 3
1. What is the significance of sampling? Name some techniques for sampling? For analyzing the data, we cannot proceed with the whole volume at once for large datasets. We need to take some samples from the data which can represent the whole population. While making a sample out of complete data, we should take the…
Read More3 Concepts Every Data Scientist Must Know Part – 2
1. Bagging and Boosting Bagging and Boosting are two different ways used in combining base estimators for ensemble learning (Like random forest combining decision trees). Bagging means aggregating the predictions of several weak learners. We can think of it combining weak learners is used in parallel. The average of the predictions of several weak learners…
Read More3 Concepts Every Data Scientist Must Know Part – 1
Central Limit Theorem We first need to introduce the normal (gaussian) distribution for central limit theorem to make sense. Normal distribution is a probability distribution that look like a bell. X-axis represents the values and y-axis represents the probability of observing these values. The sigma values represent standard deviation normal distribution is used to represent…
Read MoreMost Common Feature Scaling methods in Machine Learning
Definition Feature scaling is the process of normalizing the range of feature in a dataset. Real-world datasets often contain features that are varying in degrees of magnitude, range and units. Therefore, in order for machine learning models to interpret these features on the same scale, we need to perform scaling. Feature scaling makes the model…
Read MoreStress Detection Project using Machine Learning
Stress, tension, and misery are undermining the psychological well-being of individuals. Each individual has a justification behind having an unpleasant life. Individuals frequently discuss their thoughts via web-based entertainment stages like on Instagram as posts and stories, and on Reddit through requesting ideas about their life on subreddits. In the beyond couple of years, many…
Read MoreOutlier Detection methods in Machine Learning
Objective An outlier is an individual point of data that is distant from other points in the dataset. It is an anomaly in the dataset that may be caused by a range of errors in capturing, processing or manipulating data. Outliers in the data may cause problem during model fitting as it may inflate the…
Read MoreMissing Values Treatment methods in Machine Learning
Delete Missing Value Rows Missing values can be handled by deleting the rows or columns having null values. If columns have more than half of the rows as null then the entire columns can be dropped. The rows which are having one or more columns values as null can also dropped. Pros: A model trained…
Read MoreRestaurant Recommendation System using Machine Learning
In this article we are going to discuss about the Restaurant Recommendation System. it is an application that recommends similar restaurants to a customer according to the customer’s taste. We will learn how to build a restaurant recommendation system. This article will take you through how to build a restaurant recommendation system using Machine Learning.…
Read MoreHierarchical clustering for Machine Learning
Hierarchical clustering is another unsupervised machine learning algorithm, which is used to group the unlabeled datasets into a cluster. Hierarchical Clustering creates clusters in a hierarchical tree-like structure (also called a Dendogram) as it creates a subset of similar data in a tree-like structure in which the root node corresponds to the entire data, and…
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