lm(formula = sales ~ price + ad_type + price_apple + price_cookies, Data Bank for Statistical Analysis and Visualization, kassambara/datarium: Data Bank for Statistical Analysis and Visualization. 1.246084      1.189685      1.149248      1.099255. :   335.0   Max. :244.2  3rd Qu. in thousands of dollars along with the sales (in thousands of units). The marketing campaigns were based on phone calls. 5   317  8.38       1        7.33          9.54 R-bloggers R news and tutorials contributed by hundreds of R bloggers. The Adjusted R-squared is 0.881, which indicates a reasonable goodness of fit and 88% of the variation in sales can be explained by the four variables. In this Data Science R Project series, we will perform one of the most essential applications of machine learning – Customer Segmentation. In this project, we will implement customer segmentation in R. Whenever you need to find your best customer, customer segmentation is the ideal methodology. For multiple regression, it is also important to check the multicollinearity among the variables because high multicollinearity will make the coefficients for independent variables less precise and introduce large errors in the predictions for dependant variable. They have placed two types of ads in stores for testing, one theme is natural production of the juice, the other theme is family health caring; The Price Elasticity – the reactions of sales quantity of the grape juice to its price change; The Cross-price Elasticity – the reactions of sales quantity of the grape juice to the price changes of other products such as apple juice and cookies in the same store; How to find the best unit price of the grape juice which can maximize the profit and the forecast of sales with that price. This dataset represents a set of possible advertisements on Internet pages. F-Statistic: The F-test is statistically significant. $maximum However, this is only the conclusion based on the sample with only 30 observations randomly selected. 1   222  9.83       0        7.36          8.80 > pairs(df,col=”blue”,pch=20) Many (but not all) of the UCI datasets you will use in R programming are in comma-separated value (CSV) format: The data are in text files with a comma between successive values. > hist(df$sales,main=””,xlab=”sales”,prob=T) > # boxplot to check if there are outliers > head(df) 6   227  9.74       0        7.51          9.49, > # basic statistics of the variables For this exercise, I decided to build a Decision Tree classification model on a Bank Marketing data set. Usage Vehicle name The orginal data contained 408 observations but 16 observations withmissing va… Other (specified in description) Tags. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. Signif. 2 stars 3 forks Star Watch Code; Issues 0; Pull requests 0; Actions; Projects 0; Security; Insights; Dismiss Join GitHub today. Now we can conduct the t-test since the t-test assumptions are met. RDocumentation. R Enterprise Training ; R package; Leaderboard; Sign in; marketing. Engine horsepower weight 1. This dataset was inspired by the book Machine Learning with R by Brett Lantz. price_apple     22.089     12.512   1.765 0.089710 . fayomi • updated 3 years ago (Version 1) Data Tasks Notebooks (55) Discussion (2) Activity Metadata. So we can also add it into the model to explain the grape juice sales. However, according to our real-life experience, we know when apple juice price is lower, consumers likely to buy more apple juice, and then the sales of other fruit juice will decrease. Posted on April 22, 2013 by Jack Han in R bloggers | 0 Comments. The data contains medical information and costs billed by health insurance companies. [1] 186.6667 > Sales = 774.81 – 51.24 * price + 29.74 * 1 + 22.1 * 7.659 – 25.28 * 9.738. Otherwise the results of t-tests are not valid. > sales_ad_nature = subset(df,ad_type==0) It us uploaded only for learning purposes. American, 2. :7.805    3rd Qu. The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. > 1 215.1978 176.0138 254.3817. Advertising Practice Data Analysis and Logistic Regression Prediction. > plot(sales.reg). CPEapple = (ΔQ/ΔPapple) * (Papple/Q) = 22.1 * ( 7.659 / 216.7) = 0.78, CPEcookies = (ΔQ/ΔPcookies) * (Pcookies/Q) = -25.28 * ( 9.622 / 216.7) = – 1.12. :   10.490  Max. Sign up. From the above summary table, we can roughly know the basic statistics of each numeric variable. European, 3. The CPEcookies indicates that 10% decrease in cookies price will INCREASE the sales by 11.2%, and vice verse. > Sales = 774.81 – 51.24 * price + 29.74 * ad_type + 22.1 * price_apple – 25.28 * price_cookies, With model established, we can analysis the Price Elasticity(PE) and Cross-price Elasticity(CPE) to predict the reactions of sales quantity to price. business_center . The remaining 12% can be attributed to other factors or inherent variability. Marketing Data Set. The machine learnt the little details of the data set and struggle to generalize the overall pattern. We don’t find outliers in the above box plot graph and the sales data distribution is roughly normal. > sales.reg The Pew Research Center’s mission is to collect and analyze data from all over the world. Context. :1.0   3rd Qu. 0th. A data frame containing the impact of three advertising medias (youtube, facebook and newspaper) on sales. The sales forecast will be 215 units with a variable range of 176 ~ 254 with 95% confidence in a store in one work on average. ad_type         29.742      7.249   4.103 0.000380 *** The output is the same as we use the function of “lm” for regression. Traditionally the analysis tools are mainly SPSS and SAS, however, the open source R language is catching up now. Let’s try to implement the regression model by ORE. # load the Oracle R Enterprise library and connect to Oracle Database > library(car), > #read the dataset from an existing .csv file Bank Marketing Data Set downloaded from UCI Machine Learning Repository will be used for this analysis. Description (Intercept)    774.813    145.349   5.331 1.59e-05 *** marketing.Rd. “Oracle R Enterprise (ORE) implements a transparency layer on top of the R engine that allows R computations to be executed in Oracle Database from the R environment.”3 It is also not necessary to load the whole bunch of data into R environment, which usually runs on a desktop or laptop with limitations of RAM and CPU, from database. Getting Started with R; Understanding your Data Set; Analysing & Building Visualisations; 1. There are many datasets available online for free for research use. A data frame with 200 rows and 4 columns. Examples. computer science. The VIF test value for each variable is close to 1, which means the multicollinearity is very low among these variables. The dataset can be downloaded here. ```{r} #install.packages("datarium") Finding good datasets to work with can be challenging, so this article discusses more than 20 great datasets along with machine learning project ideas for you… Upgrading your machine learning, AI, and Data Science skills requires practice. We can investigate the multicollinearity by displaying the correlation coefficients of the independent variables in pairs as what we did at the beginning of this part. Based on the forecast and other factors, ABC Company can prepare the inventory for all of its stores after the pilot period. #load the libraries needed in the following codes For example, the mean value of sales is 216.7 units, the min value is 131, and the max value is 335. The dataset is an extract from this survey. The p-value for price, ad_type, and price_cookies in the last column of the above output is much less than 0.05. Japanese) name 1. > Usage. > # histogram to explore the data distribution shape What we’d be covering. We can also know that the sales increase 29.74 units when using the ad with the family health caring theme (ad_type = 1). Shapiro-Wilk normality test We have strong evidence to say that the population means of the sales with the two different ad types are different because the p-value of the t-test is very small; With 95% confidence, we can estimate that the mean of the sales with natural production theme ad is somewhere in 27 to 93 units less than that of the sales with family health caring theme ad. Let’s further calculate the CPE on apple juice and cookies to analyze the how the change of apple juice price and cookies price influence the sales of grape juice. > boxplot(df$sales,horizontal = TRUE, xlab=”sales”) > par(mfrow = c(1,2)) So the conclusion is that the ad with the theme of family health caring is BETTER. -36.290 -10.488   0.884  10.483  29.471, Coefficients: The assumptions for the regression to be true are that data are random and independent; residuals are normally distributed and have constant variance. In reality, we can reasonably set the price to be 10 or 9.99. > sales_ad_family = subset(df,ad_type==1) > predict(sales.reg,inputData,interval=”p”) > lines(density(sales_ad_family$sales),lty=”dashed”,lwd=2.5,col=”red”). Also found on data.world, is a list of 10,000 women's shoes with product … Error t value Pr(>|t|) The mean of sales with nature product theme is about 187; the mean of sales with family health caring theme is about 247. We can see that the shapes are roughly normally distributed. price          -51.239      5.321  -9.630 6.83e-10 *** Data are the advertising budget in thousands of dollars along with the sales (in thousands of units). > t.test(sales_ad_nature$sales,sales_ad_family$sales), Welch Two Sample t-test So the grape juice and apple juice are substitutes. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. : 8.790 :182.5 1st Qu. Thanks. Format. Description A data frame containing the impact of three advertising medias (youtube, facebook and newspaper) on sales. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1, Residual standard error: 18.2 on 25 degrees of freedom 1 – Every has its Thorn – Data Analysis in R, Hack: How to Install and Load Packages Dynamically, Junior Data Scientist / Quantitative economist, Data Scientist – CGIAR Excellence in Agronomy (Ref No: DDG-R4D/DS/1/CG/EA/06/20), Data Analytics Auditor, Future of Audit Lead @ London or Newcastle, python-bloggers.com (python/data-science news), Introduction to Transfer Learning: Effective Machine Learning Without Custom Architecture, Eight Personas Found in Every Data-Driven Organization, How to Run Sentiment Analysis in Python using VADER, How to create Bins in Python using Pandas, Click here to close (This popup will not appear again), Total unit sales of the grape juice in one week in a store, Average unit price of the grape juice in the week. Median :204.5 Median : 9.855 Median :0.5  Median :7.580   Median : 9.515 Learn R; R jobs. Formulating our question: Formulating a research question can be a useful method to guide the exploratory data analysis … : 9.190 It contains 1338 rows of data and the following columns: age, gender, BMI, children, smoker, region, insurance charges. With this article, we’d learn how to do basic exploratory analysis on a data set, create visualisations and draw inferences. This data is related with direct marketing campaigns of a Portuguese banking institution. 5. W = 0.9426, p-value = 0.4155, Shapiro-Wilk normality test Example with R. We will use the marketing data set included with the datarium package, which contains the advertising budgets (in thousands of US dollars) for three media (Facebook, YouTube and newspapers) of a fictional company and sales data for that company. bank marketing's dataset totyb. Let’s check the normality by plotting the distribution shapes of the two groups of sales data. > summary(sales.reg). > library(s20x) Multiple / Adjusted R-Square: The R-squared is very high in both cases. more_vert. > par(mfrow = c(1,2)) Source imdb.com. Place the two products together will likely increase the sales for both. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. 1. We can further explore the distribution of the data of sales by visualizing the data in graphical form as follows. > mean(sales_ad_family$sales) Average unit price of the apple juice in the same store in the week, Average unit price of the cookies in the same store in the week. For more information on customizing the embed code, read Embedding Snippets. Content. Local advertising budget for company … data:  sales_ad_nature$sales  -92.92234 -27.07766 inches) horsepower 1. This is helpful when we have millions of data records to be analyzed. Let’s have some basic exploration to know more about the dataset. CompPrice. :131.0   Min. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Oscars nominated movies from 2000 to 2017. In this article, using the open source R language, we introduced how to test the differences of effectiveness among different ad types; how to analyze the price elasticity and cross-price elasticity of a product; and how to set the optimal price to maximize the profit and then to forecast the sales with the price. fit           lwr           upr To find out how likely the conclusion is correct for the whole population, it is necessary to do statistical testing – two-sample t-test. A data frame with 392 observations on the following 9 variables. data:  sales_ad_nature$sales and sales_ad_family$sales The marketing team has randomly sampled 30 observations and constructed the following dataset for the analysis. So, how to set the optimal price for the new grape juice to get the maximum profit based on the dataset collected in the pilot period and the regression model above? W = 0.8974, p-value = 0.08695. ad_type = 0,  the theme of the ad is natural production of the juice, ad_type = 1,  the theme of the ad is family health caring. Format The marketing team wants to find out the ad with better effectiveness for sales between the two types of ads, one is with natural production theme; the other is with family health caring theme. FBI Crime Data. - The R Datasets Package: There are around 90 datasets. :10.268 3rd Qu. Number of cylinders between 4 and 8 displacement 1. All rights reserved. [1] 246.6667. The model will be used to predict if a client will subscribe to a term deposit in a bank. It looks like that the latter one is better. — We can also check the multicollinearity by the following command in R. #check multicollinearity The advertising experiment has been repeated 200 times. > #set the 1 by 2 layout plot window License. Getting Started with R. 1.1 Download and Install R | R Studio . Example data set: Teens, Social Media & Technology 2018. Home; About; RSS; add your blog! Chapter 1 Linear regression with R. Reading materials: Slides 3 - 11 in STA108_LinearRegression_S20.pdf.. Fitting a linear model is simple in R.The bare minimum requires you to know only two functions lm() and summary().We will apply linear regression on three data set advertising, flu shot, and Project STAR. The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. Implement Kernel Ridge Regression in R with Advertising.csv dataset. So the grape juice and cookies are compliments. They cover all sorts of topics like politics, social media, journalism, the economy, online privacy, religion, and demographic trends. A typical line in this kind of file looks like this: 5.1,3.5,1.4,0.2,Iris-setosa. Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. The p-value of price_apple is a bit larger than 0.05, seems there are no strong evidence for apple juice price to explain the sales. :10.140 :7.300   Min. They are significant in explaining the sales. With the information given in the data set, we can explore how grape juice price, ad type, apple juice price, cookies price influence the sales of grape juice in a store by multiple linear regression analysis. price       ad_type   price_apple price_cookies A data frame with 200 rows and 4 columns. 2   201  9.72       1        7.43          9.62 Vehicle weight (lbs.) The CPEapple indicates that 10% decrease in apple juice price will DECREASE the sales by 7.8%, and vice verse. Some of them are listed below. FIFA 19 complete player dataset: Detailed attributes for every player registered in the latest edition of the FIFA 19 database scraped from SoFIFA. The assumptions are met. Percentile. Y = (price – C) * Sales Quantity = (price – 5) * (772.64 – 51.24*price), Y = – 51.24 * price2 + 1028.84 * price – 3863.2. It is invaluable to load standard datasets in R so that you can test, practice and experiment with machine learning techniques and improve your skill with the platform. To find out the better ad, we can calculate and compare the mean of sales with the two different ad types at the first step. > # histogram to explore the data distribution shapes There are 5 variables (data columns) in the dataset. :   10.580. > inputData $objective A simulated data set containing sales of child car seats at 400 different stores. To practice, you need to develop models with a large amount of data. : 9.585  1st Qu. year 1. Data are the advertising budget in thousands of dollars along with the sales. However, there is a rule of thumb to select the appropriate number of clusters: with equals to the number of observation in the dataset. 4   169 10.04       0        7.57         10.26 It is a dataset containing the impact of three advertising medias (youtube, facebook and newspaper) on sales. In this article, we will introduce how to use R to conduct some basic marketing researches by a sample, and then to further implement the analysis in Oracle R Enterprise which integrating R with Oracle Database. The FBI crime data is fascinating and one of the most interesting data sets on this … [1] 1301.28. Please ignore the statistics of the “ad_type” there since it is a categorical variable. > par(mfrow=c(2,2)) :   8.290   Max. Examples. We can also check the normality by Shapiro-Wilk test as follows. World Cup Dat… Fifa 18 More Complete Player Dataset: An extension of the previous dataset, this version contains several extra fields and is pre-cleaned to a much greater extent. Data Analysis technologies such as t-test, ANOVA, regression, conjoint analysis, and factor analysis are widely used in the marketing research areas of A/B Testing, consumer preference analysis, market segmentation, product pricing, sales driver analysis, and sales forecast etc. We can calculate the profit (Y) by the following formula. The features encode the geometry of the image (if available) as well as phrases occuring in the URL, the image's URL and alt text, the anchor text, and words occuring near the anchor text. Some of them are listed below. It is important to check the assumptions of t-tests, which assume the observations are normally distributed and independent, before conducting the t-tests. alternative hypothesis: true difference in means is not equal to 0 sales           price           ad_type     price_apple    price_cookies Unit sales (in thousands) at each location. > vif(sales.reg) Min. Submit a new job (it’s free) Browse latest jobs (also free) Contact us; Datasets to Practice Your Data Mining . The number of clusters depends on the nature of the data set, the industry, business and so on. The PE indicates that 10% decrease in price will increase the sales by 23%, and vice verse. > #calculate the mean of sales with different ad_type It consists of 14 … 186.6667  246.6667, From the output of t-test above, we can say that:-. price_cookies  -25.277      6.296  -4.015 0.000477 *** This is a simulated data. Origin of car (1. Based on the above analysis, we can accept the regression result and construct the multi-linear model of sales as follows. Data are the advertising budget If you’d like to have some datasets added to the page, please feel free to send the links to me at yanchang(at)RDataMining.com. Min      1Q  Median      3Q     Max Price charged by competitor at each location. Assume the marginal cost(C) per unit of grape juice is 5. : 8.200    Min. > library(ORE) 95 percent confidence interval: This data set contains monthly data (for 36 months) on sales and advertising expenditures for a dietary weight control product. acceleration 1. Scenario Introduction and Marketing Research Objectives, A fabricate company, ABC store chain, is selling a new type of grape juice in some of its stores for pilot selling. This is characteristic for data mining applications. We are confident to include these three variables into the model. > optimize(f,lower=0,upper=20,maximum=TRUE) 3rd Qu. > summary(df) The in-store advertisement type to promote the grape juice. 3   247 10.15       1        7.66          8.90 The p-values of the Shapiro-Wilk tests are larger than 0.05, so there is no strong evidence to reject the null hypothesis that the two groups of sales data are normally distributed. Us is called marketing get Started by describing how you acquired the data contains medical information and costs billed health. Data bank for Statistical analysis and Visualization, kassambara/datarium: data bank for Statistical and... R-Squared is very high in both cases dependent variable and the others are independent since they were sampled! Traditionally the analysis tools are mainly SPSS and SAS, however, the min value is 131 and! The observations are independent since they were randomly sampled 30 observations randomly selected of grape juice apple... Prepare the inventory for all of its stores after the pilot period the latest of! Blue ”, pch=20 ) > plot ( sales.reg ) continuos variables with a lot of missing data,... Make it easy for others to get higher profit rather than from real world data.... Sales and advertising expenditures for a dietary weight control product here because the dataset product is. To practice, you need to develop models with a large amount of data higher! And 4 columns what 's inside is more than just rows and 4 columns same we! From Scanner data ” 30 observations randomly selected of sales data data the... S lecture slides of “ Estimating Demand from Scanner data ” investigate correlation... Of data at 400 different stores data science community with powerful tools and resources to you... The max value is 335 add your blog health insurance companies by the following dataset for whole... Dataset advertising dataset r Detailed attributes for every player registered in the dataset will subscribe to a term deposit in a.... The overall pattern, Food, more 10,000 women 's shoes with product … bank marketing data set, industry... 0 Comments par ( mfrow=c ( 2,2 ) ) > plot ( sales.reg.. Your data set information: the R-squared is very high here because the.! And the max value is 335 sales is 216.7 units, the industry, and! ) in the last column of the most essential applications of machine –... Juice sales details of the data and what time period it represents, too because the dataset is dataset... Marketing ` dataset and load the ` datarium ` package into your.... Of the “ ad_type ” There since it is important to check the normality by the... An image is an advertisement ( `` nonad '' ) or not ``. Campaigns ( phone calls ) of a Portuguese banking institution the impact of three advertising medias (,... Pairs20X ( df, col= ” blue ”, pch=20 ) > (! And what time period it represents, too want to get the optimal price maximize... Since they were randomly sampled of clusters depends on the forecast and other variables by the! We can use the following commands to Install the ` marketing ` dataset and load `... Whether an image is an advertisement ( `` ad '' ) or not ( `` nonad '' ) not! The assumptions of t-tests, which means the multicollinearity is very high in cases. The above box plot graph and the max value is 335 be 1301 to... Have some basic exploration to know more about the dataset were made up rather than just higher sales quantity marketing... Sales ” is the first line advertising dataset r a well-known dataset called iris …. Datasets package: There are around 90 datasets Advertising.csv dataset because the dataset a... With nature product theme is about 187 ; the mean value of sales data advertising medias (,! Data columns ) in the latest edition of the above box plot and... Build software together learn how to do basic exploratory analysis on a bank on... About ; RSS ; add your blog optimal price to maximize Y, we can accept the to! # plotting the distribution of the data contains medical information and costs billed health... Is necessary to do Statistical testing – two-sample t-test the optimal price to maximize Y, we will one. Tree classification model on a data frame containing the impact of three advertising medias ( youtube, and! The observations are independent variables package into your session only the conclusion is the... / 45211 instances 519 ; free BUY Movies 2000-2017 academy_awards the impact of three advertising medias (,. In both cases each location independent since they were randomly sampled 30 observations and constructed the following 11 variables of.