Hope you have enjoyed my last post about kaggle submission and you also tried to build your own machine learning model.
To continue the same spirit today I will discuss about my model submission for the Wallmart Sales Forecasting where I got a score of 3077 (rank will be 196) in kaggle.
Challenge: In this challenge, we are provided with historical sales data for 45 Walmart stores located in different regions since 2010-02-05 to 2012-11-01. Each store contains a number of departments, and we are tasked with predicting the department-wide sales for each store.
My Sollution: To solve this machine learning regression problem I followed below steps-
1) load the datasets.
While loading the datasets, I ensured that required attributes only should be there in train and feature datasets.
The important key feature which affects the sales of a store mostly is 'markdown' because this feature contains the information of festival seasons.
So it really important to do feature engineering of this attribute.
2) divide the markdown into groups
3) combine the test,train,feature datasets with markdown grouping
It's always a good approach to combine the datasets so that you get more features as a combined dataset and then you can split them accordingly your need.
Please note applying a model into a dataset is only a 20% of your task but the 60% of the task is taken by the initial steps of your machine learning model; that is EDA of the problem.
For every column of your dataset first visualize how each column is co-related to the target variable that is in our case is sales.
In this dataset there is a Date column and it means you are dealing with time series data and it need your attention to handle this data.
For handling the date with holiday we need to write some logic in such a way that our logic handle the markdown with respect to every store and it's sales.
4) handle the missing values
Always check how many missing values are present in each attribute of your dataset. For the time saving people just simply remove those. But removing the missing values affect your model accuracy.
That's why it is important to analyze the data more and find out a way to deal the missing values.
For this problem I dealed with the markdown's missing values with the mean of it's values.
5) model the datasets
After doing the EDA your 80% task is done. Next task is to build the model for your problem. I choosed linear regression and ExtraTreesRegressor to build the model.
6) save the output in kaggle format
Each competition in kaggle requires it's own submission format that we have to follow.For this challenge they ask to save the output in a csv with two columns- Id and weekly_Sales.
The first column Id is in format as store id_dept id_date and second column is our target variable-sales.
For everyone who wish to see my actual code can see it from here- my code.
I request you all to please download my notebook from above url, upload in your jupyter norebook, explore it, think about new approaches,apply different different machine learning algorithms to improve the model and share your inputs with me also.
Meanwhile Friends! Go chase your dreams, have an awesome day, make every second count and see you later in my next post.
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