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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
1. Car Trends in www.carfax.com
1
STATISTICAL DATA MINING
ISM6137.901S17
PROJECT REPORT ON
Car Trends in www.carfax.com
AUTHORS:
Jijo Johny
Deepesh Kumar
Pradeep Raj Ooralath
Majed Alghamdi
2. Car Trends in www.carfax.com
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Contents
Introduction.......................................................................................................................... 4
About www.carfax.com......................................................................................................... 5
Web Scraping ........................................................................................................................ 7
Dataset ................................................................................................................................. 9
Relationships between variables ......................................................................................... 12
Price vs Mileage...........................................................................................................................12
Price vs Engine.............................................................................................................................13
Price vs Fuel-type ................................................................................................................ 14
Price vs MPG_HWY conditioned on Engine ...................................................................................15
Correlation plot of all variables ....................................................................................................16
Regression Models .............................................................................................................. 17
Model 1............................................................................................................................... 17
Model 2............................................................................................................................... 18
Model 3............................................................................................................................... 19
Models 1, 2, 3 Comparison .................................................................................................. 20
Model 4............................................................................................................................... 21
Model 5............................................................................................................................... 22
Model 6............................................................................................................................... 23
Model 7............................................................................................................................... 24
3. Car Trends in www.carfax.com
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Hypothesis Testing .............................................................................................................. 26
Confidence Interval ............................................................................................................. 28
Conclusion:.......................................................................................................................... 30
4. Car Trends in www.carfax.com
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Introduction
The aim of this project is to analyze the trends of car prices in www.carfax.com and to
predict the variation of the prices based on their make, model, year of manufacture,
mileage run by the car, type of engine, fuel type, exterior color and interior color. The
data was obtained by scrapping the web site www.carfax.com. The scrapping was done
by using a chrome extension “Web Scraper”. The data obtained was then cleaned using R
and then various models were created in order to analyze the trend of the price and also
to predict the price on the most influencing variable. Many regression models were
created on the data and the models were compared on R squared value, AIC, BIC and
residual plots of each model. We have considered only sedans belonging to the segment.
The data in Carfax related to the below cars were extracted from the website:
1. Honda Accord
2. Toyota Camry
3. Honda Civic
4. Hyundai Elantra
5. Ford Fusion
6. Kia Optima
7. Chevrolet Malibu
8. Nissan Altima
9. Ford Mustang
5. Car Trends in www.carfax.com
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About www.carfax.com
Carfax, Inc. is a commercial web-based service that supplies vehicle history reports to
individuals and businesses on used cars and light trucks for the American and Canadian
consumers. CARFAX started with a vision - to be the leading source of vehicle history
information for buyers and sellers of used cars. Today, CARFAX has the most
comprehensive vehicle history database available in North America. Millions of
consumers trust CARFAX to provide them with vehicle history information every year.
CARFAX receives information from more than 100,000 data sources including every U.S.
and Canadian provincial motor vehicle agency plus many auto auctions, fire and police
departments, collision repair facilities, fleet management and rental agencies, and more.
CARFAX Vehicle History Reports™ are available on all used cars and light trucks model
year 1981 or later. Using the unique 17-character vehicle identification number (VIN), a
CARFAX Report is instantly generated from their database of over 17 billion records.
Every CARFAX Report contains information that can impact a consumer's decision about
a used vehicle. Some types of information that a CARFAX Report may include are:
Title information, including salvaged or junked titles
Flood damage history
Total loss accident history
6. Car Trends in www.carfax.com
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Odometer readings
Lemon history
Number of owners
Accident indicators, such as airbag deployments
State emissions inspection results
Service records
Vehicle use (taxi, rental, lease, etc.)
7. Car Trends in www.carfax.com
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Web Scraping
Web scraping was done with the chrome extension “Web Scraper”. The following
information were extracted from the website for each car:
1. Make
2. Model
3. Year
4. Mileage
5. Price
6. Engine
7. Transmission
8. Fuel type
9. MPG
10.Exterior color
11.Interior color
Web Scraping using “Web Scraper” Chrome extension:
9. Car Trends in www.carfax.com
9
Dataset
Source of Data: www.carfax.com
Number of Variables: 12
Total number of instances: 2108
Variable Type Values
Make Categorical Honda, Toyota, Hyundai, Ford, etc
Model Categorical Accord, Civic, Camry, Mustang, etc
Year Categorical
Price Numeric
Mileage Numeric
Engine Categorical 4,6 and 8 cylinder
Transmission Categorical Manual and automatic
Fuel type Categorical Gasoline, hybrid and flexible fuel type
MPG_CY Numeric MPG in cities
MPG_HWY Numeric MPG in highways
Ext Color Categorical Exterior color
Int Color Categorical Interior color
12. Car Trends in www.carfax.com
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Relationships between variables
Price vs Mileage
Price and mileage have a negative relationship.
13. Car Trends in www.carfax.com
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Price vs Engine
1. 8 cylinder engines are generally more expensive.
2. 4 cylinder and 6 cylinder engines have similar price mostly.
14. Car Trends in www.carfax.com
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Price vs Fuel-type
Flexible fuel type cars are generally less expensive than hybrid and gasoline cars.
15. Car Trends in www.carfax.com
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Price vs MPG_HWY conditioned on Engine
MPG_HWY is lower for 8 cylinder cars but the price is generally higher because they are
more powerful.
16. Car Trends in www.carfax.com
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Correlation plot of all variables
17. Car Trends in www.carfax.com
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Regression Models
Model 1
25. Car Trends in www.carfax.com
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Prediction
• smp_size <- floor(0.40*nrow(d))
• set.seed(123)
• train_ind <- sample(seq_len(nrow(d)),size=smp_size)
• train <- d[train_ind,]
• test <- d[-train_ind,]
• pred<- predict.lm(mod9,test,type="response")
• err <- pred-test$Price
• pred.prob <- err <2000
• table(pred.prob)
Model Wrongly Predicted Correctly Predictioed Accuracy
mod 5 211 1054 83.3202
mod 6 210 1055 83.3992
mod 7 183 1082 85.5336
26. Car Trends in www.carfax.com
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Hypothesis Testing
1. Carfax claims that the average price of the Ford Mustang cars they sold in the
past year is $19000. KBB, the main competitor of Carfax, believed that this value
is lower than the actual average because the average price of the Ford Mustang
cars that KBB sold in the previous year was more than $20000. So, they decided
to prove that Carfax is lying by taking a sample. (Significance level : 5%)
Ha : Average Price > $19000
H0: Average Price <= $19000
> M <- mean(mus$Price); M;
[1] 19381.24
> n <- length(mus$Price); n;
[1] 352
> s <- sd(mus$Price); s;
[1] 7927.969
> se <- s/sqrt(n); se;
[1] 422.5621
> Z <- (M - 19000)/se; Z;
[1] 0.9022073
> P <- pnorm(Z); P;
[1] 0.8165266
Since, P > Significance level, we don’t have enough evidence to reject H0. Hence,
we cannot conclude that the average price is greater than $18000.
27. Car Trends in www.carfax.com
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2. Carfax claims that the Ford Mustang cars they sell has an average MPG of 28 in
highways. KBB wants to prove that this claim is false (Significance level = 0.05).
Ha : Average MPG < 28
H0: Average MPG >=28
> M <- mean(mus$MPG_HWY); M;
[1] 26.52841
> n <- length(mus$MPG_HWY); n;
[1] 352
> s <- sd(mus$MPG_HWY); s;
[1] 2.915581
> se <- s/sqrt(n); se;
[1] 0.155401
> Z <- (M - 28)/se; Z;
[1] -9.469635
> P <- pnorm(Z); P;
[1] 1.404113e-21
Since, P < Significance level, we have enough evidence to reject H0. Hence, we
can conclude that the average MPG in highways is less than 28.
28. Car Trends in www.carfax.com
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Confidence Interval
1. Constructing a 95% confidence interval for the average price of Ford Mustang.
Based on this confidence interval, what is the minimum price that a buyer can
expect (with 95% confidence)?
> M <- mean(mus$Price); M;
[1] 19381.24
> n <- length(mus$Price); n;
[1] 352
> s <- sd(mus$Price); s;
[1] 7927.969
> se <- s/sqrt(n); se;
[1] 422.5621
> moe <- qnorm(0.975) * se; moe;
[1] 828.2066
> ci <- M + c(-moe, moe); ci;
[1] 18553.03 20209.45
Minimum price that a buyer can expect is $18553
2. Constructing a 95% confidence interval for the average mileage of Ford Mustang.
Based on this confidence interval, what is the minimum mileage that a buyer can
expect (with 95% confidence)?
> M <- mean(mus$Mileage); M;
[1] 43469.36
> n <- length(mus$Mileage); n;
[1] 352
> s <- sd(mus$Mileage); s;
[1] 33745.19
> se <- s/sqrt(n); se;
[1] 1798.625
> moe <- qnorm(0.975) * se; moe;
[1] 3525.24
> ci <- M + c(-moe, moe); ci;
[1] 39944.12 46994.60
29. Car Trends in www.carfax.com
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Minimum mileage that a buyer can expect is around 40000 miles.
3. In an annual sales report that Carfax published, they showed that the average price
of the Nissan Altima cars they sold is greater than that of Toyota Camry. Let’s see
whether this is correct by analyzing the sample we collected.
Nissan Altima:
> M <- mean(alt$Price); M;
[1] 13528.43
> n <- length(alt$Price); n;
[1] 366
> s <- sd(alt$Price); s;
[1] 4621.014
> se <- s/sqrt(n); se;
[1] 241.5443
> moe <- qnorm(0.975) * se; moe;
[1] 473.4181
> ci <- M + c(-moe, moe); ci;
[1] 13055.01 14001.85
95% confidence interval: [13055 14002]
Toyota Camry:
> M <- mean(cam$Price); M;
[1] 12268.13
> n <- length(cam$Price); n;
[1] 332
> s <- sd(cam$Price); s;
[1] 4726.381
> se <- s/sqrt(n); se;
[1] 259.3939
> moe <- qnorm(0.975) * se; moe;
[1] 508.4026
> ci <- M + c(-moe, moe); ci;
[1] 11759.73 12776.53
95% confidence interval: [11760 12777]
The confidence intervals of the average prices of Altima and Camry does not overlap.
There, we can conclude that the average price of Altima is greater than that of
Camry, with a 95% confidence.
30. Car Trends in www.carfax.com
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Conclusion:
Major factors that influence the price of a car are:
• Year: A car that is built recently have a higher price compared to other cars. 2014
built cars have a higher price over others.
• Mileage: A car that ran more fetch a lower price.
• The engine model has a major take in influencing the price.
• Exterior color influences a buyer while purchasing a car.
