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What is the difference between regression and classification?

Regression and classification are both types of supervised learning tasks in machine learning, where a model learns from labelled training data to make predictions on unseen data. The main difference between them lies in the type of output or prediction they make. Regression : In a regression task, the model is trained to predict a continuous or quantitative output. For example, predicting the price of a house based on its features (like size, location, number of rooms, etc.) is a regression task because the price is a continuous quantity that can range from any value to any value. Classification : In a classification task, the model is trained to predict a discrete or categorical output. For example, predicting whether an email is spam or not spam is a classification task because the output (spam or not spam) is a category. Use regression when you want to predict a quantity (like house prices, temperatures, sales amounts, etc.) and use classification when you want to predict...
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What is decision tree?

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A decision tree is a flowchart-like structure in which each internal node represents a feature (or attribute), each branch represents a decision rule, and each leaf node represents an outcome. The topmost node in a decision tree is known as the root node. It learns to partition on the basis of the attribute value. It partitions the tree recursively in a manner called recursive partitioning. Decision Trees are one of the easiest and popular classification algorithms to understand and interpret. It can be utilized for both classification and regression kind of problems. Here's a simple example of a decision tree: In this example, the decision tree makes predictions based on the size and price of a house. The internal nodes are decisions based on these features, the branches are the outcomes of these decisions, and the leaf nodes are the final predictions. In a machine learning context, decision trees learn from data to approximate a sine curve with a set of if-then-else decision ...
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How decision trees are used in Machine Learning?

In machine learning, decision trees are used for both classification and regression tasks. They are part of a class of algorithms called supervised learning algorithms, which means they learn from labeled training data to make predictions or decisions without being explicitly programmed to perform the task. Here's a brief overview of how decision trees are used in machine learning: Classification : Decision trees are commonly used for classification problems, where the goal is to predict which category a new observation belongs to base on its features. The decision tree algorithm builds a tree-like model of decisions based on the features in the training data. Each node in the tree represents a feature, each branch represents a decision rule, and each leaf represents an outcome, or class label. When a new observation is made, the algorithm starts at the root of the tree and moves down the tree following the decision rules that match the observation's features until it reach...
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What is random forest?

Random Forest is a popular and versatile machine learning method that is capable of performing both regression and classification tasks. It is a type of ensemble learning method, where a group of weak models combine to form a strong model. In Random Forest, the weak models are decision trees. Here's a brief overview of how Random Forest works: Bootstrap Data : Random Forest starts by selecting random samples from the dataset. This is done with replacement, meaning the same sample can be chosen multiple times. This process is known as bootstrapping. Build Decision Trees : For each bootstrap sample, a decision tree is built. At each node of the tree, a random subset of features is chosen to decide the best split. This randomness in feature selection adds to the "randomness" of the Random Forest. Make Predictions : For a classification problem, each tree in the forest gives a "vote" for the class, and the class with the most votes is the prediction of the R...
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Descriptive Analytics : Test Case 2

 Here is another example of Description Analytics based on sample data of Diwali Sales Download Data Set of Diwali Sales Download Jupyter Notebook Download the PDF File
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Descriptive Analytics : Test Case 1

  Descriptive Analytics Descriptive analytics involves summarizing historical data to gain an understanding of past performance. It includes basic data aggregation, reporting, and visualization, which help provide insights into  what has happened . To gain an understanding of past performance through basic data aggregation, reporting, and visualization, let's consider a sample scenario in the context of a fictional e-commerce business. We will analyze historical sales data to provide insights into what has happened over the past year. Download Jupyter Notebook Download PDF File
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1.7 Introduction to relevant statistical software packages and carrying out descriptive analysis through it

Introduction to relevant statistical software packages and carrying out descriptive analysis through it The use of statistical software packages is crucial for conducting data analysis and deriving meaningful insights from data. Here's a general overview of statistical software packages and how they are used for descriptive analysis: Statistical Software Packages ü       R Programming Language R is a programming language and environment designed for statistical computing and graphics. It is open-source and widely used for statistical analysis, data visualization, and machine learning. R provides a wide range of functions and packages for descriptive statistics. Common functions include mean, median, standard deviation, histograms, and summary statistics for data exploration. ü       Python with Pandas and NumPy Python is a general-purpose programming language, and libraries like Pandas and NumPy provide powerful tools for data manipulation, ...
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