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.
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. If you are looking for a Data Science mentor, I highly recommend that you start there. If you would like to learn more about the tools I used to build the solution mentioned in this case study, please see the links below:.
Sign in. Andrew Olton Follow. Understanding the Value Proposition. Towards Data Science A Medium publication sharing concepts, ideas, and codes.
Problem Solver. Towards Data Science Follow. A Medium publication sharing concepts, ideas, and codes. See responses 5. More From Medium. More from Towards Data Science. Rhea Moutafis in Towards Data Science. Taylor Brownlow in Towards Data Science.Blcd 2019
Discover Medium.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 will use this model to understand the properties of products and stores which play a key role in increasing sales.
BigMart Sales Prediction
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.Disparity bilateral filter
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.About the Big Mart Sales category.
Error encountered while executing Mode calculation in Big Mart Sales prediction problem. Scoring param does not available in linear regression. Redundant variables in tutorial code. Problem while solving Big Mart problem using Linear Regression. Problem to understand data exploring using r. My Solution - score Improving model score apart from gbm and randomforest. Not updating leaderboard. How does the scoring system work in leader board?
Bigmart sales using R. Filling NaN values. Do these scores make sense? Unable to combine train and test data set in Big mart Sales problem. Not able to execute the following code in python version '3.
Response variable column missing from test file.BigMart Sales Data Regression
Operations on train data vs the test data. Unable to download the Big Mart Data Sales. What does mode x. Onlyy columns are visible after spliiting the combined data set back into train and test! Plausible error in RMSE calculation by server. How to improve score.
Feature selection and Model Tuning in R.Meri randi maa ki najayaj rishte ki story
Problem in submitting the solution.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.
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Set final Submission.
Set Submissions to check private score.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 aim is to build a predictive model and find out the sales of each product at a particular store. Using this model, BigMart will try to understand the properties of products and stores which play a key role in increasing sales.
So the idea is to find out the properties of a product, and store which impacts the sales of a product. Moving to nominal categorical variable, lets have a look at the number of unique values in each of them. Lets impute the former by the average weight of the particular item.
We explored some nuances in the data in the data exploration section. Lets move on to resolving them and making our data ready for analysis. We will also create some new variables using the existing ones in this section. Products with higher visibility are likely to sell more.
But along with comparing products on absolute terms, we should look at the visibility of the product in that particular store as compared to the mean visibility of that product across all stores.
Practice Problem : BigMart Sales Prediction - 2
This will give some idea about how much importance was given to that product in a store as compared to other stores. Dropping some columns to avoid dummy variable trap and again dividing data to training and test set. A generic function which takes the algorithm and data as input and makes the model, performs cross-validation and generates submission.
Series alg1. If you notice the coefficients, they are very large in magnitude which signifies overfitting. To cater to this, lets use a ridge regression model.
Series alg2. Looks like the Ridge Regression increased the magnitude of coefficients rather than decreasing them. Series alg3. This tells us that the model is slightly overfitting. Series alg4. Series alg5. Gradient Boost Model provides the best solution in the models trained above. A little more parameter tuning can give more good results.
With this we come to the end of this section. If you want all the codes for model building in an iPython notebook format, you can download the same from my Github Repository.
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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.Delfleet one
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 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.
Project 3: Big Mart Sales Prediction (Part 1)
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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