golfdaa.blogg.se - I, Eda by Howard R. Price

Possibly most customers made purchases during lunch hour between 12:00pm - 2:00pm The company receives the highest number of orders at 12:00pm.The number of orders received by the company tends to increases from Monday to Thursday and decrease afterward.There are no transactions on Saturday between 1st Dec 2010 - 9th Dec 2011.The month with the lowest sales is undetermined as the dataset consists of transactions until 9th December 2011 in December Therefore, the TOP 5 countries (including UK) that spend the most money on purchases are as follow → United Kingdom, Netherlands, Ireland (EIRE), Germany, France As the company receives the highest number of orders from customers in the UK (since it is a UK-based company), customers in the UK spend the most on their purchases.Therefore, the TOP 5 countries (including UK) that place the highest number of orders are as follow → United Kingdom, Germany, France, Ireland (EIRE), Spain The company receives the highest number of orders from customers in the UK (since it is a UK-based company).The customer with the highest money spent on purchases comes from Netherlands.

The customer with the highest number of orders comes from the United Kingdom (UK).

Data Cleaning (a.k.a data preprocessing).

To give a brief overview, this post is dedicated to 5 sections as follow: For technical reference, please refer to my notebook on Kaggle anytime you want to have a more detailed understanding of the codes.

We’ll focus on the overall workflow of EDA, visualization and its results. Now that we have already understood the “WHAT and WHY” aspects of EDA, let’s examine a dataset together and go through the “HOW” that will eventually lead us to discover some interesting patterns, as we’ll see in the next section.

Interpret the model output and test it’s assumptions.

Spot any potential anomalies in data to avoid feeding wrong data to a machine learning model.

Make sure business stakeholders ask the right questions - often by exploring and visualizing data - and validate their business assumptions with thorough investigation.

This is exactly where the importance of Exploratory Data Analysis (EDA)(as defined by Jaideep Khare) comes in which, unfortunately, is a commonly undervalued step as part of the data science process.ĮDA is so important for 3 reasons (at least) as stated below: Yet, it’s easier to just dive into applying some fancy machine learning algorithms -and Voila! You got the prediction - without first understanding the data. In general explanation, data science is nothing more than using advanced statistical and machine learning techniques to solve various problems using data.