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Approach and Solution to break in Top 20 of Big Mart Sales prediction

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Practice problems or data science projects are one of the best ways to learn data science. You don’t learn data science until you start working on problems yourself.

BigMart Sales Prediction practice problem was launched about a month back, and 624 data scientists have already registered with 77 among those making submissions. If you’re finding it difficult to start or if you feel stuck somewhere, this article is meant just for you. Today I am going to take you through the entire journey of getting started with this data set.

I hope that this article will help more and more people start their data science journey!

Big Mart Sales Prediction

We will explore the problem in following stages:

  1. Hypothesis Generation – understanding the problem better by brainstorming possible factors that can impact the outcome
  2. Data Exploration – looking at categorical and continuous feature summaries and making inferences about the data.
  3. Data Cleaning – imputing missing values in the data and checking for outliers
  4. Feature Engineering – modifying existing variables and creating new ones for analysis
  5. Model Building – making predictive models on the data

Without further ado, lets get started!


1. Hypothesis Generation

This is a very pivotal step in the process of analyzing data. This involves understanding the problem and making some hypothesis about what could potentially have a good impact on the outcome. This is done BEFORE looking at the data, and we end up creating a laundry list of the different analysis which we can potentially perform if data is available. Read more about hypothesis generation here.

The Problem Statement

Understanding the problem statement is the first and foremost step. You can view this in the competition page but I’ll iterate the same here:

The data scientists at BigMart have collected 2013 sales data for 1559 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. Let’s think about some of the analysis that can be done and come up with certain hypothesis.

The Hypotheses

I came up with the following hypothesis while thinking about the problem. These are just my thoughts and you can come-up with many more of these. Since we’re talking about stores and products, lets make different sets for each.

Store Level Hypotheses:

  1. City type: Stores located in urban or Tier 1 cities should have higher sales because of the higher income levels of people there.
  2. Population Density: Stores located in densely populated areas should have higher sales because of more demand.
  3. Store Capacity: Stores which are very big in size should have higher sales as they act like one-stop-shops and people would prefer getting everything from one place
  4. Competitors: Stores having similar establishments nearby should have less sales because of more competition.
  5. Marketing: Stores which have a good marketing division should have higher sales as it will be able to attract customers through the right offers and advertising.
  6. Location: Stores located within popular marketplaces should have higher sales because of better access to customers.
  7. Customer Behavior: Stores keeping the right set of products to meet the local needs of customers will have higher sales.
  8. Ambiance: Stores which are well-maintained and managed by polite and humble people are expected to have higher footfall and thus higher sales.

Product Level Hypotheses:

  1. Brand: Branded products should have higher sales because of higher trust in the customer.
  2. Packaging: Products with good packaging can attract customers and sell more.
  3. Utility: Daily use products should have a higher tendency to sell as compared to the specific use products.
  4. Display Area: Products which are given bigger shelves in the store are likely to catch attention first and sell more.
  5. Visibility in Store: The location of product in a store will impact sales. Ones which are right at entrance will catch the eye of customer first rather than the ones in back.
  6. Advertising: Better advertising of products in the store will should higher sales in most cases.
  7. Promotional Offers: Products accompanied with attractive offers and discounts will sell more.

These are just some basic 15 hypothesis I have made, but you can think further and create some of your own. Remember that the data might not be sufficient to test all of these, but forming these gives us a better understanding of the problem and we can even look for open source information if available.

Lets move on to the data exploration where we will have a look at the data in detail.


2. Data Exploration

We’ll be performing some basic data exploration here and come up with some inferences about the data. We’ll try to figure out some irregularities and address them in the next section. If you are new to this domain, please refer our Data Exploration Guide.

The first step is to look at the data and try to identify the information which we hypothesized vs the available data. A comparison between the data dictionary on the competition page and out hypotheses is shown below:

0. data dictionary

We can summarize the findings as:


You will invariable find features which you hypothesized, but data doesn’t carry and vice versa. You should look for open source data to fill the gaps if possible. Let’s start by loading the required libraries and data. You can download the data from the competition page.

import pandas as pd
import numpy as np

#Read files:
train = pd.read_csv("train.csv")
test = pd.read_csv("test.csv")

Its generally a good idea to combine both train and test data sets into one, perform feature engineering and then divide them later again. This saves the trouble of performing the same steps twice on test and train. Lets combine them into a dataframe ‘data’ with a ‘source’ column specifying where each observation belongs.

data = pd.concat([train, test],ignore_index=True)
print train.shape, test.shape, data.shape

1. shape

Thus we can see that data has same #columns but rows equivalent to both test and train. One of the key challenges in any data set is missing values. Lets start by checking which columns contain missing values.

data.apply(lambda x: sum(x.isnull()))

2. missing

Note that the Item_Outlet_Sales is the target variable and missing values are ones in the test set. So we need not worry about it. But we’ll impute the missing values in Item_Weight and Outlet_Size in the data cleaning section.

Lets look at some basic statistics for numerical variables.


3. describe

Some observations:

  1. Item_Visibility has a min value of zero. This makes no practical sense because when a product is being sold in a store, the visibility cannot be 0.
  2. Outlet_Establishment_Years vary from 1985 to 2009. The values might not be apt in this form. Rather, if we can convert them to how old the particular store is, it should have a better impact on sales.
  3. The lower ‘count’ of Item_Weight and Item_Outlet_Sales confirms the findings from the missing value check.

Moving to nominal (categorical) variable, lets have a look at the number of unique values in each of them.

data.apply(lambda x: len(x.unique()))

4. unique

This tells us that there are 1559 products and 10 outlets/stores (which was also mentioned in problem statement). Another thing that should catch attention is that Item_Type has 16 unique values. Let’s explore further using the frequency of different categories in each nominal variable. I’ll exclude the ID and source variables for obvious reasons.

#Filter categorical variables
categorical_columns = [x for x in data.dtypes.index if data.dtypes[x]=='object']
#Exclude ID cols and source:
categorical_columns = [x for x in categorical_columns if x not in ['Item_Identifier','Outlet_Identifier','source']]
#Print frequency of categories
for col in categorical_columns:
    print '\nFrequency of Categories for varible %s'%col
    print data[col].value_counts()

5. categories

The output gives us following observations:

  1. Item_Fat_Content: Some of ‘Low Fat’ values mis-coded as ‘low fat’ and ‘LF’. Also, some of ‘Regular’ are mentioned as ‘regular’.
  2. Item_Type: Not all categories have substantial numbers. It looks like combining them can give better results.
  3. Outlet_Type: Supermarket Type2 and Type3 can be combined. But we should check if that’s a good idea before doing it.


3. Data Cleaning

This step typically involves imputing missing values and treating outliers. Though outlier removal is very important in regression techniques, advanced tree based algorithms are impervious to outliers. So I’ll leave it to you to try it out. We’ll focus on the imputation step here, which is a very important step.

Note: We’ll be using some Pandas library extensively here. If you’re new to Pandas, please go through this article.

Imputing Missing Values

We found two variables with missing values – Item_Weight and Outlet_Size. Lets impute the former by the average weight of the particular item. This can be done as:

#Determine the average weight per item:
item_avg_weight = data.pivot_table(values='Item_Weight', index='Item_Identifier')

#Get a boolean variable specifying missing Item_Weight values
miss_bool = data['Item_Weight'].isnull() 

#Impute data and check #missing values before and after imputation to confirm
print 'Orignal #missing: %d'% sum(miss_bool)
data.loc[miss_bool,'Item_Weight'] = data.loc[miss_bool,'Item_Identifier'].apply(lambda x: item_avg_weight[x])
print 'Final #missing: %d'% sum(data['Item_Weight'].isnull())

7. imputation1

This confirms that the column has no missing values now. Lets impute Outlet_Size with the mode of the Outlet_Size for the particular type of outlet.

#Import mode function:
from scipy.stats import mode

#Determing the mode for each
outlet_size_mode = data.pivot_table(values='Outlet_Size', columns='Outlet_Type',aggfunc=(lambda x:mode(x).mode[0]) )
print 'Mode for each Outlet_Type:'
print outlet_size_mode

#Get a boolean variable specifying missing Item_Weight values
miss_bool = data['Outlet_Size'].isnull() 

#Impute data and check #missing values before and after imputation to confirm
print '\nOrignal #missing: %d'% sum(miss_bool)
data.loc[miss_bool,'Outlet_Size'] = data.loc[miss_bool,'Outlet_Type'].apply(lambda x: outlet_size_mode[x])
print sum(data['Outlet_Size'].isnull())

8. imputation2

This confirms that there are no missing values in the data. Lets move on to feature engineering now.

4. Feature Engineering

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.

Step 1: Consider combining Outlet_Type

During exploration, we decided to consider combining the Supermarket Type2 and Type3 variables. But is that a good idea? A quick way to check that could be to analyze the mean sales by type of store. If they have similar sales, then keeping them separate won’t help much.


9. outlet type

This shows significant difference between them and we’ll leave them as it is. Note that this is just one way of doing this, you can perform some other analysis in different situations and also do the same for other features.

Step 2: Modify Item_Visibility

We noticed that the minimum value here is 0, which makes no practical sense. Lets consider it like missing information and impute it with mean visibility of that product.

#Determine average visibility of a product
visibility_avg = data.pivot_table(values='Item_Visibility', index='Item_Identifier')

#Impute 0 values with mean visibility of that product:
miss_bool = (data['Item_Visibility'] == 0)

print 'Number of 0 values initially: %d'%sum(miss_bool)
data.loc[miss_bool,'Item_Visibility'] = data.loc[miss_bool,'Item_Identifier'].apply(lambda x: visibility_avg[x])
print 'Number of 0 values after modification: %d'%sum(data['Item_Visibility'] == 0)

10. visib 1

So we can see that there are no values which are zero.

In step 1 we hypothesized that 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. This will give some idea about how much importance was given to that product in a store as compared to other stores. We can use the ‘visibility_avg’ variable made above to achieve this.

#Determine another variable with means ratio
data['Item_Visibility_MeanRatio'] = data.apply(lambda x: x['Item_Visibility']/visibility_avg[x['Item_Identifier']], axis=1)
print data['Item_Visibility_MeanRatio'].describe()

11. visb 2

Thus the new variable has been successfully created. Again, this is just 1 example of how to create new features. I highly  encourage you to try more of these, as good features can drastically improve model performance and they invariably prove to be the difference between the best and the average model.

Step 3: Create a broad category of Type of Item

Earlier we saw that the Item_Type variable has 16 categories which might prove to be very useful in analysis. So its a good idea to combine them. One way could be to manually assign a new category to each. But there’s a catch here. If you look at the Item_Identifier, i.e. the unique ID of each item, it starts with either FD, DR or NC. If you see the categories, these look like being Food, Drinks and Non-Consumables. So I’ve used the Item_Identifier variable to create a new column:

#Get the first two characters of ID:
data['Item_Type_Combined'] = data['Item_Identifier'].apply(lambda x: x[0:2])
#Rename them to more intuitive categories:
data['Item_Type_Combined'] = data['Item_Type_Combined'].map({'FD':'Food',

12. item_type

Another idea could be to combine categories based on sales. The ones with high average sales could be combined together. I leave this for you to try.

Step 4: Determine the years of operation of a store

We wanted to make a new column depicting the years of operation of a store. This can be done as:

data['Outlet_Years'] = 2013 - data['Outlet_Establishment_Year']

13. years of oper

This shows stores which are 4-28 years old. Notice I’ve used 2013. Why? Read the problem statement carefully and you’ll know.

Step 5: Modify categories of Item_Fat_Content

We found typos and difference in representation in categories of Item_Fat_Content variable. This can be corrected as:

#Change categories of low fat:
print 'Original Categories:'
print data['Item_Fat_Content'].value_counts()

print '\nModified Categories:'
data['Item_Fat_Content'] = data['Item_Fat_Content'].replace({'LF':'Low Fat',
                                                             'low fat':'Low Fat'})
print data['Item_Fat_Content'].value_counts()

14. fat content1

Now it makes more sense. But hang on, in step 4 we saw there were some non-consumables as well and a fat-content should not be specified for them. So we can also create a separate category for such kind of observations.

#Mark non-consumables as separate category in low_fat:
data.loc[data['Item_Type_Combined']=="Non-Consumable",'Item_Fat_Content'] = "Non-Edible"

15. fat content 2

Step 6: Numerical and One-Hot Coding of Categorical variables

Since scikit-learn accepts only numerical variables, I converted all categories of nominal variables into numeric types. Also, I wanted Outlet_Identifier as a variable as well. So I created a new variable ‘Outlet’ same as Outlet_Identifier and coded that. Outlet_Identifier should remain as it is, because it will be required in the submission file.

Lets start with coding all categorical variables as numeric using ‘LabelEncoder’ from sklearn’s preprocessing module.

#Import library:
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
#New variable for outlet
data['Outlet'] = le.fit_transform(data['Outlet_Identifier'])
var_mod = ['Item_Fat_Content','Outlet_Location_Type','Outlet_Size','Item_Type_Combined','Outlet_Type','Outlet']
le = LabelEncoder()
for i in var_mod:
    data[i] = le.fit_transform(data[i])

One-Hot-Coding refers to creating dummy variables, one for each category of a categorical variable. For example, the Item_Fat_Content has 3 categories – ‘Low Fat’, ‘Regular’ and ‘Non-Edible’. One hot coding will remove this variable and generate 3 new variables. Each will have binary numbers – 0 (if the category is not present) and 1(if category is present). This can be done using ‘get_dummies’ function of Pandas.

#One Hot Coding:
data = pd.get_dummies(data, columns=['Item_Fat_Content','Outlet_Location_Type','Outlet_Size','Outlet_Type',

Lets look at the datatypes of columns now:


16. final dtypes

Here we can see that all variables are now float and each category has a new variable. Lets look at the 3 columns formed from Item_Fat_Content.


17. final fat content compare

You can notice that each row will have only one of the columns as 1 corresponding to the category in the original variable.

Step 7: Exporting Data

Final step is to convert data back into train and test data sets. Its generally a good idea to export both of these as modified data sets so that they can be re-used for multiple sessions. This can be achieved using following code:

#Drop the columns which have been converted to different types:

#Divide into test and train:
train = data.loc[data['source']=="train"]
test = data.loc[data['source']=="test"]

#Drop unnecessary columns:

#Export files as modified versions:

With this we come to the end of this section. If you want all the codes for exploration and feature engineering in an iPython notebook format, you can download the same from my GitHub repository.


4. Model Building

Now that we have the data ready, its time to start making predictive models. I will take you through 6 models including linear regression, decision tree and random forest which can get you into Top 20 ranks in this competition (I mean ranks as of today because after reading this article, I’m sure many new leaders will emerge).

Lets start by making a baseline model. Baseline model is the one which requires no predictive model and its like an informed guess. For instance, in this case lets predict the sales as the overall average sales. This can be done as:

#Mean based:
mean_sales = train['Item_Outlet_Sales'].mean()

#Define a dataframe with IDs for submission:
base1 = test[['Item_Identifier','Outlet_Identifier']]
base1['Item_Outlet_Sales'] = mean_sales

#Export submission file

Public Leaderboard Score: 1773

Seems too naive for you? If you look at the public LB now, you’ll find 4 players below this number. So making baseline models helps in setting a benchmark. If your predictive algorithm is below this, there is something going seriously wrong and you should check your data.

If you participated in AV datahacks or other short duration hackathons, you’ll notice first submissions coming in within 5-10 mins of data being available. These are nothing but baseline solutions and no rocket science.

Taking overall mean is just the simplest way. You can also try:

  1. Average sales by product
  2. Average sales by product in the particular outlet type

These should give better baseline solutions.

Since I’ll be making many models, instead of repeating the codes again and again, I would like to define a generic function which takes the algorithm and data as input and makes the model, performs cross-validation and generates submission. If you don’t like functions, you can choose the longer way as well. But I have a tendency of using functions a lot (actually I over-use sometimes :D). So here is the function:

#Define target and ID columns:
target = 'Item_Outlet_Sales'
IDcol = ['Item_Identifier','Outlet_Identifier']
from sklearn import cross_validation, metrics
def modelfit(alg, dtrain, dtest, predictors, target, IDcol, filename):
    #Fit the algorithm on the data
    alg.fit(dtrain[predictors], dtrain[target])
    #Predict training set:
    dtrain_predictions = alg.predict(dtrain[predictors])

    #Perform cross-validation:
    cv_score = cross_validation.cross_val_score(alg, dtrain[predictors], dtrain[target], cv=20, scoring='mean_squared_error')
    cv_score = np.sqrt(np.abs(cv_score))
    #Print model report:
    print "\nModel Report"
    print "RMSE : %.4g" % np.sqrt(metrics.mean_squared_error(dtrain[target].values, dtrain_predictions))
    print "CV Score : Mean - %.4g | Std - %.4g | Min - %.4g | Max - %.4g" % (np.mean(cv_score),np.std(cv_score),np.min(cv_score),np.max(cv_score))
    #Predict on testing data:
    dtest[target] = alg.predict(dtest[predictors])
    #Export submission file:
    submission = pd.DataFrame({ x: dtest[x] for x in IDcol})
    submission.to_csv(filename, index=False)

I’ve put in self-explanatory comments. Please feel free to discuss in comments if you face difficulties in understanding the code. If you’re new to the concept of cross-validation, read more about it here.

Linear Regression Model

Lets make our first linear-regression model. Read more on Linear Regression here.

from sklearn.linear_model import LinearRegression, Ridge, Lasso
predictors = [x for x in train.columns if x not in [target]+IDcol]
# print predictors
alg1 = LinearRegression(normalize=True)
modelfit(alg1, train, test, predictors, target, IDcol, 'alg1.csv')
coef1 = pd.Series(alg1.coef_, predictors).sort_values()
coef1.plot(kind='bar', title='Model Coefficients')


1. lin reg

Public LB Score: 1202

We can see this is better than baseline model. But if you notice the coefficients, they are very large in magnitude which signifies overfitting. To cater to this, lets use a ridge regression model. You should read this article if you wish to learn more about Ridge & Lasso regression techniques.

Ridge Regression Model:

predictors = [x for x in train.columns if x not in [target]+IDcol]
alg2 = Ridge(alpha=0.05,normalize=True)
modelfit(alg2, train, test, predictors, target, IDcol, 'alg2.csv')
coef2 = pd.Series(alg2.coef_, predictors).sort_values()
coef2.plot(kind='bar', title='Model Coefficients')


2. ridge reg

Public LB Score: 1203

Though the regression coefficient look better now, the score is about the same. You can tune the parameters of the model for slightly better results but I don’t think there will be a significant improvement. Even the cross-validation score is same so we can’t expect way better performance.

Decision Tree Model

Lets try out a decision tree model and see if we get something better.

from sklearn.tree import DecisionTreeRegressor
predictors = [x for x in train.columns if x not in [target]+IDcol]
alg3 = DecisionTreeRegressor(max_depth=15, min_samples_leaf=100)
modelfit(alg3, train, test, predictors, target, IDcol, 'alg3.csv')
coef3 = pd.Series(alg3.feature_importances_, predictors).sort_values(ascending=False)
coef3.plot(kind='bar', title='Feature Importances')


3. dt 1

Public LB Score: 1162

Here you can see that the RMSE is 1058 and the mean CV error is 1091. This tells us that the model is slightly overfitting. Lets try making a decision tree with just top 4 variables, a max_depth of 8 and min_samples_leaf as 150.

predictors = ['Item_MRP','Outlet_Type_0','Outlet_5','Outlet_Years']
alg4 = DecisionTreeRegressor(max_depth=8, min_samples_leaf=150)
modelfit(alg4, train, test, predictors, target, IDcol, 'alg4.csv')
coef4 = pd.Series(alg4.feature_importances_, predictors).sort_values(ascending=False)
coef4.plot(kind='bar', title='Feature Importances')

4. dt2

Public LB Score: 1157

You can fine tune the model further using other parameters. I’ll leave this to you.

Random Forest Model

Lets try a random forest model as well and see if we get some improvements. Read more about random forest here.

from sklearn.ensemble import RandomForestRegressor
predictors = [x for x in train.columns if x not in [target]+IDcol]
alg5 = RandomForestRegressor(n_estimators=200,max_depth=5, min_samples_leaf=100,n_jobs=4)
modelfit(alg5, train, test, predictors, target, IDcol, 'alg5.csv')
coef5 = pd.Series(alg5.feature_importances_, predictors).sort_values(ascending=False)
coef5.plot(kind='bar', title='Feature Importances')


5. rf1

Public LB Score: 1154

You might feel this is a very small improvement but as our model gets better, achieving even minute improvements becomes exponentially difficult. Lets try another random forest with max_depth of 6 and 400 trees. Increasing the number of trees makes the model robust but is computationally expensive.

predictors = [x for x in train.columns if x not in [target]+IDcol]
alg6 = RandomForestRegressor(n_estimators=400,max_depth=6, min_samples_leaf=100,n_jobs=4)
modelfit(alg6, train, test, predictors, target, IDcol, 'alg6.csv')
coef6 = pd.Series(alg6.feature_importances_, predictors).sort_values(ascending=False)
coef6.plot(kind='bar', title='Feature Importances')


6. rf2

LB Score: 1152

Again this is an incremental change but will help you get a jump of 5-10 ranks on leaderboard. You should try to tune the parameters further to get higher accuracy. But this is good enough to get you into the top 20 on the LB as of now. I tried a basic GBM with little tuning and got into the top 10. I leave it to you to refine with score with better algorithms like GBM and XGBoost and try ensemble techniques.

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.


End Notes

This article took us through the entire journey of solving a data science problem. We started with making some hypothesis about the data without looking at it. Then we moved on to data exploration where we found out some nuances in the data which required remediation. Next, we performed data cleaning and feature engineering, where we imputed missing values and solved other irregularities, made new features and also made the data model-friendly by one-hot-coding. Finally we made regression, decision tree and random forest model and got a glimpse of how to tune them for better results.

I believe everyone reading this article should attain a good score in BigMart Sales now. For beginners, you should achieve at least a score of 1150 and for the ones already on the top, you can use some feature engineering tips from here to go further up. All the best to all!

Did you find this article useful? Could you make some more interesting hypothesis? What other features did you create? Were you able to get a better score with GBM & XGBoost? Feel free to discuss your experiences in comments below or on the discussion portal and we’ll be more than happy to discuss.

You want to apply your analytical skills and test your potential? Then participate in our Hackathons to compete with many Data Scientists from all over the world.

This is article is quiet old now and you might not get a prompt response from the author. We would request you to post this comment on Analytics Vidhya Discussion portal to get your queries resolved.


  • Excellent article, The explanations were clear and you have also left the reader enough room to try & experiment on their own. Great work! Thanks Aarshay.

  • venugopal says:

    Good One

  • Vamsi says:

    good info on the approach for solving problems for beginners

  • Abhishek says:

    A very detailed information about how to start any Analysis. Thank you very much for the article. It would be really grateful if you can explain this in R.

    • Aarshay Jain says:

      Thanks for your feedback. I would love to do the same in R but I’m a bit crunched on bandwidth right now.
      I think once the concept is clear, language is just a matter of time. I think you should take it as a challenge, do this in R and even share it with everyone through the discussion forum. Trust me the learning which you’ll have this way will be much more. You can always seek help through discussion forum if you face challenges, but I highly encourage you to try it out. You’re going to love it 🙂
      Ready for the challenge?

  • Margie says:

    Excellent! Thank you for a very clear approach that doesn’t give away too much while helping a beginner get their feet into the game. I’m looking forward to using this approach in R when I get a chance.

    • Aarshay Jain says:

      You can actually use it for the same problem and post a submission. You’ll definitely get a good rank.
      Also, you can share the R-code with the group 🙂

  • Thanh Le says:

    Very nice article. Regarding the imputation of missing values for Item_Weight and Outlet_Size, we can look at the column Item_Identifier and Outlet_Identifier to reveal the secret 🙂

    • Aarshay Jain says:

      Thanks.. I’ve actually Item_Identifier for imputing Item_Weight. Regarding Outlet_Size, the values are missing for all instances of same outlet so Outlet_Identifier might not help much in this case.

      • Ram says:

        Hi Aarshay, Thank you for the nice article and the Python code.

        I am trying to do the same processing, using R packages.

        1. Item Weight – I am able to do the mean Imputation using the package ‘plyr’.
        2. Outlet_Size: plyr cannot calculate the mode, hence I had to use the ‘h2o’ package.

        Can someone suggest other ways to do these imputations (other than mice and knnImputation)?

        # Numeric: Impute the NA Item_Weights using ddply. (mean weight).
        data = ddply(data, “Item_Identifier”, transform,
        Item_Weight = ifelse(is.na(Item_Weight),mean(Item_Weight,na.rm=T),Item_Weight))

        # Categorical: Impute the NA Outlet_Size using h2o.impute. (mode of the Outlet Size).
        data = as.data.frame(h2o.impute(as.h2o(data), “Outlet_Size”, “mode”, by=c(“Outlet_Type”)))

  • shan says:

    An excellent and informative article.
    In the following lines, I may be missing something:
    # Determing the mode for each
    outlet_size_mode = data.pivot_table(values=’Outlet_Size’, columns=’Outlet_Type’,aggfunc=(lambda x:mode(x).mode[0]))

    AttributeError: ‘tuple’ object has no attribute ‘mode’

    Thanks a lot

    • Aarshay Jain says:

      I think its a version issue. Upgrade to the latest version of SciPy.

      Someone else had this issue and it got resolved this way. For detail read the first comment on this article:

      Let me know if this doesn’t work.


      • shan says:

        Thanks for reply. Just wondering , is it possible to upgrade a package say SciPy only , within anaconda , or have to wait for next version of anaconda distribution to get latest SciPy version.

        If we want to predict the sales as Average sales by product (baseline option 1 suggested),
        we can get Average sales by product as

        asbp = train.pivot_table(values=’Item_Outlet_Sales’, columns=’Item_Identifier’)
        base2 = test[[‘Item_Identifier’,’Outlet_Identifier’]]

        Now, what is the shortest way to add ‘Item_Outlet_Sales’ from asbp to base2 before submission.


        • Aarshay Jain says:

          Yes it is possible. Use “conda update SciPy” in the terminal/command prompt.

          For the second case, you can use the apply function. Also, use index in asbp instead of column.

          asbp = train.pivot_table(values=’Item_Outlet_Sales’, index=’Item_Identifier’)
          base2 = test[[‘Item_Identifier’,’Outlet_Identifier’]]
          base2[‘Item_Outlet_Sales’] = train[‘Item_Identifier’].apply(lambda x: asbp[x])

          Try this out. Let me know if this doesn’t work.


          • shan says:


            Thanks for responding.
            Tried out :
            asbp = train.pivot_table(values=’Item_Outlet_Sales’, index=’Item_Identifier’)
            base2 = test[[‘Item_Identifier’,’Outlet_Identifier’]]
            base2[‘Item_Outlet_Sales’] = train[‘Item_Identifier’].apply(lambda x: asbp[x])

            After executing the lines :
            Its displaying NaN values in Item_Outlet_Sales of base2

            train[‘Item_Identifier’].apply(lambda x: asbp[x]) is fine. Its not getting
            inserted in base2. under ‘Item_Outlet_Sales’.
            May be I am missing something.


            • Aarshay Jain says:


              Since base2 is based on the test set, you should do:
              base2[‘Item_Outlet_Sales’] = test[‘Item_Identifier’].apply(lambda x: asbp[x])

              My bad.. I should have noticed this earlier. Hope this works.


  • shan says:

    Hi ..

    Thanks it worked fine.
    For Average sales by product in the particular outlet type ( Baseline Option2,), will we proceed to multiindex .

    asbpot = train.pivot_table(values=’Item_Outlet_Sales’, index=[‘Item_Identifier’,’Outlet_Identifier’])
    base3 = test[[‘Item_Identifier’,’Outlet_Identifier’]]

    ## The following result in errors :
    base3[‘Item_Outlet_Sales’] = test[‘Item_Identifier’].apply(lambda x: asbpot[x])

    Thanks ..

    • Aarshay Jain says:

      It won’t work like this. In this case, there are essentially 2 options:
      1. Multi-Indexing
      2. Using both index and columns

      You used multi-indexing while creating the asbpot variable but not while looking it up as in ‘asbpot[x]’, x is just a string. Check out point #5 in this article (http://www.analyticsvidhya.com/blog/2016/01/12-pandas-techniques-python-data-manipulation/) for how to use multi-indexing.

      Another option is using Item_Identifier as the index and Outlet_Type as the column. Then you have a dataframe and you can index using a combination of variables in the entire series.

      Try these out. Let me know if this doesn’t work.

  • shan says:


    For the Multi-Indexing , option, if we write
    asbpot = train.pivot_table(values=’Item_Outlet_Sales’, index=[‘Item_Identifier’,’Outlet_Identifier’])
    base3 = test[[‘Item_Identifier’,’Outlet_Identifier’]]

    for i,row in base3.loc[:,[‘Item_Identifier’,’Outlet_Identifier’]].iterrows():
    ind = tuple([row[‘Item_Identifier’],row[‘Outlet_Identifier’]])
    base3.loc[i,’Item_Outlet_Sales’] = asbpot.loc[ind,’Item_Outlet_Sales’]

    It is generating error


    • Aarshay Jain says:

      Have you tried it exactly the way it is shown there?

      base3.loc[i,’Item_Outlet_Sales’] = asbpot.loc[ind].values[0]

      There is note below which says:
      The .values[0] suffix is required because, by default a series element is returned which has an index not matching with that of the dataframe. In this case, a direct assignment gives an error.

      Let me know if this works..

      • shan says:

        Hi Aarshay ,

        I have tried this earlier:
        asbpot = train.pivot_table(values=’Item_Outlet_Sales’, index=[‘Item_Identifier’,’Outlet_Identifier’])
        base3 = test[[‘Item_Identifier’,’Outlet_Identifier’]]

        for i,row in base3.loc[:,[‘Item_Identifier’,’Outlet_Identifier’]].iterrows():
        ind = tuple([row[‘Item_Identifier’],row[‘Outlet_Identifier’]])
        # base3.loc[i,’Item_Outlet_Sales’] = asbpot.loc[ind,’Item_Outlet_Sales’]
        base3.loc[i,’Item_Outlet_Sales’] = asbpot.loc[ind].values[0]

        Its generating Traceback :

        ‘IndexingError: Too many indexers’


        • Aarshay Jain says:

          I just noticed, you are using ‘Outlet_Identifier’. If you read the article carefully, it says the grouping should be done on ‘Outlet_Type’.

          Grouping on Outlet_Identifier doesn’t make any sense because these are unique for every row. You should use ‘Outlet_Type’.

          Since we have done one-hot-coding, the column has been divided. So either you should use the original column or recombine this.

          Let me know if this doesn’t work,

  • nipun says:

    I would like to know the necessity to split the categorical columns like Item_Fat_Content to dummy variables. Is it necessary? I guess the model should be able to predict in the same way as having 0,1,2,3… values instead of multiple columns with 0,1 values?

  • sray agarwal says:

    boosting and RF is not working in R. Can someone try boosting (GBM or ada boost) in R please

    • Aarshay Jain says:

      Hello Sray,

      I’m afraid this is not the right place to voice this concern. Please start a new thread on the discussion forum giving details of the error you are getting. You can access the discussion forum here: http://discuss.analyticsvidhya.com


      • sray agarwal says:

        The concern was regarding the case discussed in this blog, I tried solving the case using R and not python and faced an issue. Since it was on this case, I posted my concern/error here itself

        • Aarshay Jain says:

          No worries at all. I’ll be glad to help but I’m not an expert in R. Also, I recommend posting on the discussion forum for various reasons:
          1. It allows more freedom of expression and you can post your code fragment with snapshots
          2. It will be visible to a lot more people who can jump in to help you out.

          So it might be a good idea to post your code with the error message there and I’m sure your issue will be resolved by the R guys.


  • Sabarinath says:

    Excellent and simple!

  • Jason says:

    Hi, Aarshay. Thanks for sharing the knowledge and help the beginner like myself to get started with real-world data science problem.
    May I suggest you to add a paragraph to talk about what can we gain from the model, essentially answering the question “How could we use the model to understand the properties of products and stores which play a key role in increasing sales?”

    • Aarshay Jain says:

      A very pertinent question indeed. As per my understanding, there are 2 types of scenarios of application of predictive modeling:
      1. interpretability is much more important than.
      2. accuracy is more important and interpretability can be compromised.

      Case 1 would involve linear or simple non-linear models like logistic regression or decision tree. The latter involves algorithms like random forests, GBM, etc. In this case, we might not be able to directly estimate the impact like in linear models, but we get some idea about relative importance of variables through the feature importance scores plotted above.

      Please feel free to share your thoughts and discuss further.

      • Jason says:

        I agreed with your opinions. In some case, the main purpose is to accurately predict the results. As in this problem, my understanding is that we need get some insights from the data to increase the sales. So we need to interpret the model and conclude some guideline on how to do that. I know that to completely answer this question need deeper understanding about the business logic, however is there any common strategy or steps we could follow? For example, from the feature importance plot, we can tell the Item_MRP is the most important feature. Does that mean we should reduce/increase the retail price in order to increase the sales?

        • Aarshay Jain says:

          Agree with your thought process. But as I said, this is not a linear model. So if MRP is important, this doesn’t say that it has a linear relationship with sales. The relationship is non-linear and also dependent on other properties. One way to check this is to look at the splits in the trees and try to make sense about the relations. There might be some pre-defined libraries as well, but I’m sure. I’ll look into this and get back to you in case I find something.

          • Ram says:

            Good discussion. I would like to add this:

            I see that the data does not include the ‘Sale Price’, which is normally different from MRP. If we include the ‘Sale Price’, and compare it to the avg ‘Sale price’ at competitors (external data), we will come up with a more useful result. In that case, lowering the ‘Sale Price’ might increase the sales.

            • Aarshay Jain says:

              Yes sales price has potential for improving the model. But since we don’t have that information in the data, can’t really do anything about it 🙂

  • Praveen Gupta Sanka says:

    Got to know the framework of predictive model.
    Once again a great article Aarshay..
    With the help of this article I am able to do Home Loan Prediction problem as well.

  • Amit says:

    Very briefly all the basic approach has been discussed. Thank you for the post. If we scale out data based on range and mean will it have a positive impacts ?

    • Aarshay Jain says:

      Scaling helps more in regression models rather than tree based models. There is no fixed impact of scaling on tree-based models.

  • Nagarjun srinivas says:

    Hi Aarshay Jain,
    Thank you for such a nice article , we are new to python and this is our first project

    we have a problem with respect to the following code

    “outlet_size_mode = data.pivot_table(values=’Outlet_Size’, columns=’Outlet_Type’,aggfunc=(lambda x:mode(x).mode[0]) )”

    we are getting the following error
    RuntimeWarning: The input array could not be properly checked for nan values. nan values will be ignored.
    “values. nan values will be ignored.”, RuntimeWarning)

    unorderable types: float() > str()

    Can you please help us with this ??

    Thanks and regards
    Nagarjun Sriniavs

  • Ramasubramaniam says:

    Tried to reproduce your approach. Facing issue with item-identifier FDR: SHould this be coded as ‘Drinks’ or “Food’?

  • Nishant Gupta says:

    TypeError: unorderable types: float() > str()

    #Determing the mode for each
    outlet_size_mode = data.pivot_table(values=’Outlet_Size’, columns=’Outlet_Type’,aggfunc=(lambda x:mode(x).mode[0]) )

    I am using Python 3.5 version Spyder platform. I am new to Python , unable to resolve the error, tried a lot, kindly help.

    According to me the Outlet_Size is an object type variable that’s why it is not able to find mode of it .

    I tried using label encoder but it should not have null values.

  • Hardi Patel says:

    Wonderful article !
    Very useful concept for my project.
    Do you have any idea to implement hadoop Map Reduce in python for this data set for random forest, decision tree, linear regression and Ridge regression.
    I need this for my final major project.
    Thanks in advance

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