code feat

"The best thing about data is that it tells a story." - Naveen Jain Today let's have a look at another machine learning model know as Logistic Regression. Logistic Regression is a statistical model where the outcome is predicted as binary such as YES or NO, based on the previous/train_dataset. ( Please open the images in a new tab or try to zoom-in ) import required models read your dataframe visualize your dataset create Logistic Regression model split your data into train and test dataset fit your train dataset into the regression model predict future values visualize your predicted values Github link for pdf:  click here

Today let's look at one of the machine learning model which can predict the prices of house based on the described data. We'll look at the linear regression model for which we'll follow a few steps such as: import required modules read the csv file plot the graph building the linear regression model to fit/train the data predict value with a parameter have a new data file for value prediction ( to view the images either open it in a new tab or zoom-in  please! )

 For large data inserts I can suggest you few things like: Use trigger(PL/SQL) Use APPEND hint Remove indexes on tables Firstly, while you use triggers in the table it could leave data to be logically corrupt. And it will then perform insert in a very conventional way. Which is a time consuming process and won’t helps us! Secondly, using APPEND hint will help us to an extend. So, APPEND hint tells the optimizer to perform a direct-insert into the table, which improves the performance. Now there is a way which we could achieve this by minimizing the Redo generation. What Redo do is; it basically ensures the recoverability of data to the database. It writes down every transaction to the archive log. Let’s take a scenario, where if the database is running on the NOARCHIVELOG mode, using APPEND hint will reduce the redo generation i.e; it won’t write into the archive log anymore and thus increases the speed. But then it won’t be able to recover at any point in time if your data is ambiguou

Indexes are used to retrieve data from the database in a very efficient and fast way. Here are the advantages and disadvantages of index on tables; Click on the link to know more:   Click here

To begin with, List Comprehension is an elegant and easiest way for creating list based on the existing list. It is also a compact and faster way of creating lists. Let’s look at some examples: Click on the link for more explanation:  Link to my Space  

“ Data really powers everything that we do .” — Jeff Weiner In the 21st century, Data is one of the most valuable entity anyone can have! There is loads-and-loads of data generated everyday. And to process this huge amount of data we need people who have expertise in it, who by the way are called as Data Engineers. Data Engineer collects the raw data, process it for further use; but we need an Analytic process which will automatically predict the data based on the previous one. And here's how 'Machine Learning' comes into the picture. "Machine Learning allows us to make highly accurate predictions based on the Historical Dataset which is used to train the machine learning model." Today let us look at a similar ML model to predict the 'Salary' of Employees based on 'YearsofExperience'. (P.S: I've provided pdf link at the very bottom of this page for clear understanding) 1) import the required modules 2) read the csv file 3) plot the graph 4) us