The Problem is we need to evaluate our model performance and how well our trained model can be use to predict new data So, the Solution is we need to split our data into training and testing sets. Training data (in-sample data) will be used to train our model and
In this blog we will learn about Linear Regression and Multiple Linear Regression and how we can implement it int Python In Linear Regression (LR) we used one independent variable for prediction. We represent a linear model by following equation y = b0 + b1 * xb0: the interceptb1: the
Web scraping is a computer software technique of extracting information from websites. This technique mostly focuses on the transformation of unstructured data (HTML format) on the web into structured data (database or spreadsheet). We deal with HTML tags when performing scrapping. Below tags are often used in scrapping: -> Document
In EDA we get a better understanding of the data, we determine the relationships between variable and summarize the data, find important columns that give us a good prediction accuracy. Descriptive StatisticsIt gives basic statistics of the overall data. Pandas function describe() help us in that. Box plot are a
In machine learning data pre-processing or data cleaning is an important step. Because if your data is cleaned means that if there are no null values, our machine learning model will learn more about our data and find more patterns in the data. In this we convert our data into
In this article we will explore dataset and gain some insights about it via Python library Pandas. So let’s get started… There are various formats for a dataset .csv, .json, . xlsx etc. It can be stored on local machine. For loading datasets we will use Python built-in library PANDAS. Read Data
In this tutorial we will explore R language which is a programming language written in Function format, used for statistical analysis. In this article we’ll explore this language and try to analyse churn data using R. This data is focused on those customers who stop doing business with the company.