Big mart sales prediction dataset

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Big mart sales prediction dataset

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Practice Problem: Big Mart Sales III

Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit Fetching latest commit…. I would also be providing a step-by-step approach of dealing with untidy dataset and preparing it for the ultimate aim of model building. Tools used - Python — 3. You signed in with another tab or window. Reload to refresh your session.

You signed out in another tab or window.In a previous postI wrote about an approach that I take to creating value with my data science project. To quickly recap and summarize what I said in that post, the goal of Data Science is to empower better decision making. Doing this requires that we have the empathy to ensure that we ask the right questions and that we use the right information.

When juxtaposed against the Value Proposition Canvas, data science projects can be seen as products that meet the needs of our customers namely decision makingdeal with the challenges associated to making those decisions and maximize the benefits to be gained from making the right decisions.

You can take a look using the link below. The data scientists at BigMart have collected sales data for products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The data contained in the dataset is as follows:. Using what we know to create our customer profile we get:.

The graph below shows the importance of various features in the dataset:. Adjusting the forecast primarily means selecting the outlet type that will yield the most promising forecast.

In doing this, I decided that it was best to cycle through the existing outlet and their respective configuration due to the fact that there are only 10 BigMart outlets.

The code for doing so is as follows:. Generating our new forecast was fairly simple and was done like so:. After running the program I wrote, the following recommendation was produced:. Tying all of this back to what I mentioned previously about Value Proposition Design and Data Science projects, we can summarize what we have designed like so:.

Note that in this example, our solution not only solves a problem for the staff at Big Martbut is also affects their customers. Thinking about those affected by the decisions our products support is vital to creating the right product.

Data Science Project in Python on BigMart Sales Prediction

Unfortunately, this is not how life works. Even though were provided with sales data, were are still not sure of the seasonality of the shopping habits observed, which can certainly have an impact on the quality of the recommendation produced. A better version of this system would be able to find the best placement options for multiple products while allowing users to prioritize one product over another.

I hope that this post gave you a clear and practical approach to using creating value with your Data Science to projects and I hope that you learned something new. As usual I welcome your feedback and look forward to producing more content. I would like to end this post by giving a shout to some very important people.

Firstly I would like to thank the lovely folks at Data Helpers for making themselves available for questions, guidance and data science help in general.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Given sales data for products across 10 stores of the Big Mart chain in various cities the task is to build a model to predict sales for each particular product in different stores.

2.2. Sales Prediction - Python

The train and test data, which can be found at the link given above, contain the following variables:. For no particular reason I decided to tackle this challenge in R. A first analysis of the data, treatment of missing values and outliers, some feature engineering, and, finally, ordering of the predictor variables by their importance in fitting a random forest model was performed with the script AnalyzeAndClean. The original data contain five different levels for the fat content: LF, low fat, Low Fat, reg, and Regular.

Further, certain types of non-consumables, i. Clearly, this makes no sense. Hence, we introduce an new fat level None for non-consumables. Let's explore those weights a little. Looking at a boxplot of the weights grouped by the outlet identifier we see that OUT and OUT have not reported any weight data: Fortunately, all the wares on offer in those stores are also sold elsewhere.

Fortunately, this successfully filled all the mising values: Looking at those plots one notices that all the medians, boxes and whiskers are identical to each other. Have those practice data been faked by any chance?

The sales data are for the year Also, the data contain the year in which each shop was established. For convenience, we replace that value by the number of years each shop has been in existence before To differentiate between them we introduced a new factor with four price levels: LowMediumHigh. To tackle that problem, let's explore sales in various outlets.

big mart sales prediction dataset

Counting how many sales where reported by each outlet. This is neatly illustrated by a boxplot: Grouping sales by the type of outlet and the years it has existed, or the type of outlet and its size, we see that there is a clear distinction in sales figures between grocery stores and supermarkets. This is confirmed if we look at sales figures across various item categories:. However, the various types of supermarkets cannot be distinguished that easily.

This is probably due to other factors, e. In particular, sales in the one Type 2 supermarket in the data are somewhat low. This may be due to the fact that it is still fairly new, having been founded four years ago. Coming back to the lower sales figures for grocery stores, from the description of the data it is not immediately clear why that is so.

A reasonable assumption is that grocery stores are much smaller than supermarkets and therefore only have a reduced selection of wares on offer. This is confirmed by a simple count of item identifiers in each outlet.The data scientists at BigMart have collected sales data for products across 10 stores in different cities.

The data also includes certain attributes of each product and store. The objective is to build a predictive model and find out the sales of each product at a particular store.

big mart sales prediction dataset

Big Mart will use this model to understand the properties of products and stores which play a key role in increasing sales. The model performance will be evaluated on the basis of its prediction of the sales for the test data. This is a crucial step in the ML process. It involves understanding the problem and making some hypothesis about what factors could potentially affect the outcome of the problem statement.

The first step in this section is to look at the available data and see whether we have the data to test the hypotheses that we formed. The available data might also inspire new hypotheses. It is generally a good idea to combine both train and test datasets into one, perform feature engineering and then divide them again.

Note that the missing values Outcome variable comes from the test dataset, which is normal as those are the values we are trying to predict. Below is a more visualise way of finding the missing values. There are products and 10 outlet stores. We want to return the unique values and frequency for each of these categorical variables object. We will exclude the ID and source variable for obvious reason.

This steps involve imputing missing values and treating outliers. Treating outliers are important for regression techniques although advanced tree based algorithms are impervious to them.

In the data exploration section, we decided to consider combining the Supermarket Type2 and Type3 variables. In order to check if this is a good idea we can analyse the mean sales by the type of store. The above shows significant difference between Supermarket Type2 and Type3, therefore, we will leave them separate as it is.

We have decided to treat the 0 like missing information and impute it with mean visibility of that product. Previously we have hypothesised that products with higher visibility are likely to sell more. We should also look at the visibility of the product in that particular store relative to the mean visibility of that product across all stores.

This will give us a sense of how important the product is in that particular store relative to other stores. It might be a good idea to combine the categories. One way could be to assign a new category to each. The latest year within our data is so we can use this and the establishment year variable to calculate the years of operation of a store. The result shows that store in our dataset are 4 — 28 years old. Since scikit-learn only accepts numerical variableswe need to convert all categories of nominal variables into numeric types.

Final step of this section is to convert the data back to train and test datasets. We also need to do some final tidying of deleting some columns before and after the split. Your email address will not be published. Notify me of follow-up comments by email. Notify me of new posts by email. Objective The objective is to build a predictive model and find out the sales of each product at a particular store.

Metric The model performance will be evaluated on the basis of its prediction of the sales for the test data. Hypothesis This is a crucial step in the ML process.Please check the data set. New Data has been added along with the previous one. New file name : Alcohol consumption.

While we don't know the context in which John Keats mentioned this, we are sure about its implication in data science. While you would have enjoyed and gained exposure to real world problems in this challenge, here is another opportunity to get your hand dirty with this practice problem powered by Analytics Vidhya.

This hackathon aims to provide a professional setup to showcase your skills and compete with their peers, learn new things and achieve a steep learning curve.

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This contest is purely for learning and practicing purpose and hence no participant is eligible for prize or AV points. You are encouraged to share your approach and code file with the community. Where can I get support?

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Help us by providing valuable Feedback. Set final Submission. Set Submissions to check private score.Sales prediction is a very common real life problem that each company faces at least once in its life time. If done correctly, it can have a significant impact on the success and performance of that company. The course will equip you with the skills and techniques required to solve regression problems in R. You will be provided with sufficient theory and practice material to hone your predictive modeling skills.

We would highly recommend taking the course in the order in which it has been designed to gain the maximum knowledge from it. This is an introductory course and this does not include any placement support. Once you have worked on a few data science projects and hackathons, you can always apply to jobs on Analytics Vidhya portal.

Enroll for free. This course assumes that you have familiarity with R. Analytics Vidhya provides a community based knowledge portal for Analytics and Data Science professionals.

The aim of the platform is to become a complete portal serving all knowledge and career needs of Data Science Professionals. Who should take this course? This course is meant for people looking to learn solving regression problems using R.

big mart sales prediction dataset

Do I need to install any software before starting the course? You will need to download and install R and RStudio What is the refund policy? The course is free of charge.

Do I need to take the modules in a specific order? Do I get certificate upon completion of the course? This is a free course and therefore there is no certificate involved. What is the fee for this course? How long I can access the course? You will have access to the course for a duration of 6 months. Is there any placement support? We suggest moving this party over to a full size window. You'll enjoy it way more.

Go Fullscreen.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again.

If nothing happens, download the GitHub extension for Visual Studio and try again. A perfect project to learn Data Analytics and apply machine learning algorithms to predict the outlet production sales. The data scientists at BigMart have collected sales data for products across 10 stores in different cities.

Data Science Case Study: Optimizing Product Placement in Retail (Part 1)

Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and find out the sales of each product at a particular store.

Create a model by which Big Mart can analyse and predict the outlet production sales. It is the perfect project for learning Data Analytics. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. A perfect project to learn Data Analytics and apply machine learining algorithms to predict the outlet production sales. Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again.

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Latest commit Fetching latest commit…. Big-Mart-Sales-Prediction A perfect project to learn Data Analytics and apply machine learning algorithms to predict the outlet production sales. Given Data sets Train. Create a model by which Big Mart can analyse and predict the outlet production sales It is the perfect project for learning Data Analytics. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window.


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