gent Performance Analysis: Identified Anny as the most efficient agent and recommended her for a raise or bonus. Highlighted areas where David and Henry's performance could be improved through training or reevaluation. Data-Driven Recommendations: Proposed acquiring more cars fueled by petrol due to increasing popularity. Advocated purchasing additional cars from the "Maruti" brand based on their substantial presence in top-selling cars. Suggested focusing on cars with powerful engines available in manual transmission. Resource Optimization: Identified opportunities to optimize employee resources based on the distribution of sales channels. Predictive Modeling: Successfully built predictive models using linear regression techniques. Demonstrated the ability to predict car prices based on odometer readings and manufacturing years. Data Manipulation and Programming: Showcased adeptness in data manipulation using R's dplyr library. Employed programming skills to extract meaningful insights from complex data. Effective Communication: Presented complex technical findings in a clear, organized, and engaging manner. Translated data insights into actionable recommendations, demonstrating effective communication skills. Skills Demonstrated: Data Analysis: Proficiently analyzed large datasets, uncovering actionable insights to guide decision-making. Statistical Analysis: Utilized statistical methods in Excel and R to draw conclusions from data trends and patterns. Programming: Demonstrated programming skills in R, utilizing libraries like dplyr for data manipulation and predictive modeling. Problem-Solving: Applied analytical thinking to address real-world challenges, transforming data into actionable solutions. Communication: Effectively conveyed technical findings through clear and concise presentations, facilitating informed decision-making. Strategic Thinking: Provided recommendations grounded in data analysis to drive strategic initiatives and optimize resource allocation. Collaboration: Contributed as a team member in a group project, showcasing the ability to work collaboratively and deliver results. Attention to Detail: Illustrated meticulous attention to detail in data manipulation, modeling, and presentation creation.

1. BIG DATA FINAL
PRESENTATION
Boris Menshikov

2. Part One:DataAnalytics
CaseStudy:UsedCarAgency
1.Introduction:
1. Manager'sneedfor anonlinesystemtofacilitate workintheagency.
2. Currentuseofspreadsheetsfor datacollection.
3. Objective:PerformBusinessIntelligenceandDataAnalyticsusingR.
2.DataDescription:
1. Datasetcontaininginformation aboutusedcars.
2. FieldsincludeName,Model,Year,SellingPrice,KmDriven,FuelType,SellerType,
3. TransmissionType,Owner,Mileage,EngineCapacity,Horsepower,Torque,NumberofSeats,DealerName.
3.Project Requirements:
1. DataanalysisusingRprogramming andExcel.
4.Step1:StatisticalAnalyticsusingExcel:
1. CleandatasetandanalyzeCarAgencydatausingExcel.
2. Resultspresentedingraphsandtables.
5.Step2:Perspective AnalysisusingR:
1. CleandatasetandanalyzewithR usingggplot2package.
2. CompareresultswithExcelanalysis.
6.Step3:Predictive AnalyticsusingR:
1. Performpredictiveanalysis onCarBusinessusingLinearPredictionorLogistic Regression.
2. Compareresultswithprevioussteps.
7.Step4:PrepareaPower Presentation:
1. Createapresentationsummarizingfindings (10points).
8.AdditionalRequirements:
1. Cleaningandnormalizationofdata.
2. Detailedreportonanalysis.
3. ProvideRcodeandExcelfilesseparately.
9.AnswerQuestions usingR:
1. Identifypopularcarmodels,top-sellingdealers,averageprices,newest/oldestcars,etc.
10.Predict CarPrice:
1. Predictcarpricebasedonodometer.
11.Predict CarPrice withYear:
1. Predictcarpricebasedonodometerandyearofmanufacture.

7. ANNY IS THE MOST EFFICIENT AGENT
AMONGST OTHERS, DAVID AND HENRY ARE
FAR BEHIND. HENRY IS EXTREMELY
INEFFICIENT AGENT -> CONCLUSION: ANNY
DESERVES RAISE OR BONUS, HENRY CAN BE
FIRED OR SENT TO TRAINING. (AND
PROBABLY DAVID AS WELL)
PERSPECTIVE ANALYSIS
USING R
The code:

8. • VAST MAJORITY OF CARS ARE
EITHER ON DIESEL OR
PETROL, OTHER TYPES TAKE
UP SIGNIFICANTLY SMALLER
PORTION. PETROL CARS
GAINING MORE POPULARITY
EACH YEAR -> CONCLUSION:
ACQUIRE MORE CARS BASED
ON PETROL FUEL TO MATCH
ONGOING DEMAND.
TABLE 2:
The code:

9. • MOST POPULAR CAR IS
MARUTI ERTIGA VDI AND
MARTUGA BRAND IS HOLDS
SUBSTANTIAL PART IN TOP
10 CARS -> CONCLUSION:
PURCHASE MORE "MARUTI"
BRAND CARS TO MEET
DEMAND.
TABLE 3:
The code

10. • MANUAL CARS STOCK IS
MORE DEVELOPED THAN
AUTOMATIC CARS, CARS
WITH MORE ENGINE POWER
ARE MOSTLY MANUAL AND
MORE AFFORDABLE ->
CONCLUSION: PURCHASE
CARS WITH POWERFUL
ENGINE ONLY IN MANUAL
TRANSMISSION TYPE.
TABLE 4:
The code:

11. • BUSINESS MAKES ALMOST
EVERY SALE BY INDIVIDUAL
AND WITHOUT DEALER
SELLING -> CONCLUSION:
THE NEED OF SEVERAL
EMPLOYEES MUST BE
DOUBTED AND REVIEWED.
TABLE 5:
The code:

12. TABLE 6:
• MOST OF OUR CARS SOLD ARE
RANGING FROM LOW TO MIDDLE
CLASS CARS ACCORDING TO THEIR
PRICE WITH SOME SMALL NUMBER
OF HIGH-END VEHICLES WHICH
SPIKES THE GRAPH ->
CONCLUSION: FOCUS ON LOW-
MID CLASS TYPE OF CARS.
The code:

13. FOR OUR FIRST 3 OBJECTIVES WE
HAVE ONLY 2 VARIABLES IN THE
MODEL (Y, X). WE USE THE R
CLASS LM TO CREATE A LINEAR
MODEL THAT FITS ITSELF
THROUGH THE GRADIENT
DESCENT ALGORITHM BASED OF
THE ERROR FUNCTION.
WE PUT AS PREDICTION VARIABLE (Y) OUR
TARGET PREDICTION FEATURES (N_SALES,
AVG_SALE, TOT_SALES) AND AS X THE
TARGET YEAR.
Here we can see the p-value and the R-squared error of
our model, in relation to the final chosen coefficients
(weights of our variables in the equation).
We can visualize our model by plotting it into a graph.
Predict Analytics:

14. WE CAN SEE THAT OUR
MODEL APPEARS TO BE
UNDERFITTING, AS THE
DATA INDICATES A CURVE
TREND OVER TIME, BUT WE
ARE NOT USING A
POLYNOMIAL MODEL.

15. THERE IS A MODEL WHERE
WE CAN EVALUATE THE
NUMBER OF FIRSTOWNER
CAR SALES TO BE MADE
ACCORDING TO YEAR AND
AVERAGE YEARLY SALE
AMOUNT. LET’S PLOT THE
CORRELATION BETWEEN
THE 3 VARAIBLES:

16. • OUR MODEL IS CLEARLY A
CONTINUOUS PLANE GOING
THROUGH OUR 3-
DIMENSIONAL SPACE.
NEXT STEP IS TO
CREATE THE MODEL
AND PLOT THE
RESULT.
The way of building the model is always the same, per
each of them: choose the target variable and the X
features. It is possible to include all features we want to
predict, from 1 to n. Our model formula is

17. PREDICTS ANALYTICS USING R
The data table:

19. HERE WE CAN
SEE THE
YEARLY CARS
STATS

20. IN THIS PART
WE IMPORT THE
FILE INTO R
STUDIO AND
SAVE IT AS A
VARIABLE TO
READ AS A .CSV
THEN VIEW IS
USED TO VIEW
THE DATA IN
TABULAR
FORMAT

21. GOING FURTHER IS TO
CONVERT THE FILE
FORMAT TO
DATAFRAME, BECAUSE
WE WILL HAVE MORE
POSSIBILITIES AND IT IS
EASIER TO MANIPULATE
THE DATA. NEXT, WE
ALSO VIEW THE RESULT
IN TABULAR FORM

22. FOR EACH
EXERCISE, THE
LIBRARY DPLYR
WILL BE USED,
BECAUSE OF ITS
GREAT
FUNCTIONALITY
IN HANDLING
THE DATA.

23. THE FIRST EXERCISE
HERE WE CHOOSE A FILE WITH WHICH I
WILL WORK, THEN COUNT THE NUMBER
OF EACH MODEL AND DISPLAY THE MOST
POPULAR DUE TO THE CUT BY 1, AND
SORT FROM LARGEST TO SMALLEST.

24. THE SECOND EXERCISE
WE SELECT OUR DATAFRAME, GROUP THE DATA BY DEALER AND COUNT THE
NUMBER OF MODELS OF EACH MODEL AND SELECT SLICE TWO TO GET DATA
THAT DOES NOT RELATE TO INDIVIDUAL SALES

25. THE THIRD EXERCISE
AT THIS POINT WE DECIDED TO DISPLAY TWO COLUMNS,
THE MODEL OF THE CAR AS WELL AS ITS AVERAGE PRICE,
BY MEANS OF THE FUNCTION AGGREGATE.

26. THE FORTH EXERCISE
WE FIND THE MINIMUM YEAR AND THEN USING THE FILTER FUNCTION FIND A LINE
WHERE THE YEAR COINCIDES WITH THE MINIMUM YEAR AND EQUATE THE CUT TO
1 TO GET ONLY ONE LINE, WITH ONE MODEL, THIS WILL HELP US IF WE HAVE A
LOT OF CARS WITH THE SAME YEAR, SUCH AS A LITTLE FURTHER WHERE WE USE
EXACTLY THE SAME CODE BUT WE ARE LOOKING FOR THE MAXIMUM YEAR.
BECAUSE THERE ARE A LOT OF 2020 CARS AND WE ONLY WANT TO GET 1 RESULT,
WE MAKE THE SLICE EQUAL TO ONE

27. THE FIFTH EXERCISE
HERE WE DECIDED TO GIVE TWO POSSIBLE SOLUTIONS TO THIS PROBLEM. WHERE
THE MINIMUM PRICE CORRESPONDS TO THE MILEAGE AND VICE VERSA. THE
MINIMUM ELEMENTS I FOUND WITH THE FUNCTION MIN, AND THEN SUBSTITUTED
THEM IN THE FILTER TO FIND THE NECESSARY ROWS.

28. THE SIXTH EXERCISE
HERE WE FIND THE AMOUNT EARNED BY THE DEALER WITH
THE NAME INDIVIDUAL DUE TO THE AGGREGATE
FUNCTION AND SPECIFYING THE PRICE AS THE ITEM OVER
WHICH THE SUM FUNCTION WILL BE CONDUCTED AND THE
DEALER AS AN UNDERSTANDING OF WHO EARNED WHAT
AMOUNT.

29. THE SEVENTH EXERCISE
WE FIND THE SUBSET FOR EACH OF THE DEALERS EXCEPT THE INDIVIDUAL AND
COUNT THE NUMBER OF TRANSMISSIONS FOR EACH TO SEE WHAT TYPE OF CAR
EACH DEALER HAS THE MOST POPULAR

30. LET’S
MOVE TO
THE
NEXT
TASK
THE MAIN GOAL IS TO PREDICT THE ANY CAR PRICE
BASED ON THE ODOMETER + PREDICT THE ANY
CAR PRICE BASED ON THE ODOMETER AND
YEAR OF MANUFACTURE

31. FIRST WE CREATE A SUBSET (TABLE) WITH
ONLY THE VALUE WITH THE NAME OF A
PARTICULAR MACHINE (MARUTI SWIFT VDI)

32. THEN WE GET A SUMMARY OF A FORMULA
A + B * N

33. AND THEN WE GET THE RELATIONSHIP
RESULT

34. THEN WE USE THE FUNCTION LM TO DETERMINE THE
RELATIONSHIP BETWEEN THE PRICE AND THE
ODOMETER. OUTPUT THE RESULT AND SEE THE
"ESTIMATE" VALUE. USE THE FORMULA A + B * N TO
DISPLAY THE RESULT.

35. THIS RESULT IS AN
APPROXIMATE
CALCULATION OF HOW
MUCH THE CAR WILL
COST BASED ON THE
VALUE N(ANY) WE SET.
IN THE NEXT PROBLEM
WE DO THE SAME BUT
ADD THE SECOND
ARGUMENT TO THE
FORMULA A + B*N +
C*N1.
The summary

36. THE FINAL
RESULT

37. THANK YOU FOR YOUR
ATTENTION!