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Data Analytics Report Assignment Sample

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The assignment is being done to evaluate the case study where the demand for used cars in India is being evaluated. This evaluation is being done to bring advantages and values to the decisions of the company Used Cars R US which is based in the land of Bangalore. The cars that are being used are being evaluated based on a number of parameters which are being used to predict the trends of the used car market in India. The company is in the market to buy used cars from individual users of the cars and then sell these used cars to sellers and retailers. The trends of the demand for used cars are being evaluated so that there can be an enhanced influx of profits for the company. The data set which contains all the essential data in a bulk manner for the used cars in India are being used to bring essential insights for the report.

Discussion and Data Analysis

The data set contains bulk data on the different parameters of used cars in India. The data are first cleaned so that there can be authentic insights obtained without any anomalous data. These data when cleaned are used to draw insights into the data set with the help of statistical methods. The data are being analysed based on the conditions of evaluation. The insights of the data are being analysed with the help of descriptive analytics where the central tendencies and many other descriptive components of the parameters of the data set are evaluated. There are some columns and rows in the data set where the data is empty and data cleaning procedures are being followed to clean the data set so that essential insights can be obtained. The use of software MS Excel is being used to analyse the data of the used cars to bring insights into the market of used cars in India to help the company in making essential and useful decisions. The trends of the data are being forecasted with the help of predictive analytics where the terms of the market are being predicted for the advantage of the company ( (2019)). The visualisation tools that are being used to visualize the evaluations are all used to prejudice the condition of the market. The records of the car brand where there are certain cells which are empty are being cleared out for the quality analysis of the dataset. The parameter of the data set is grouped in different requirements to evaluate the influence of the variables on the analysis of the data.

Parameters such as selling price, ownership of the cars, made of the cars which represents the brand of the used cars, the year in which the car was being bought, the colour of the car, the working medium of the car and the kind of fuel the car's intakes are all essential parameters which are useful to reveal the demands of the cars, along with parameters such as horsepower provided, kilometers travelled and engine capacity of the cars which are useful to describe the quality of the cars which are to be bought by the company.

Graph of selling price

Figure 1: Graph of selling price

(Source: self-created in MS EXCEL)

The above histogram cure is used to analyse the trends of selling prices against the different brands of cars that are available. The trend of the selling price of Mercedes Benz is the only one which is rising to 100% which means that the company should focus more on the brand of Mercedes Benz (Divya and Girisha, 2021). The use of descriptive statistics is being used to define the descriptive values of the sling price parameter which is used to evaluate the insights of the selling price of the data set.

Descriptive statistics

Figure 2: Descriptive statistics

(Source: self-created in MS EXCEL)

The mean of the selling price obtained is 1679802.053, this denotes that the average of the selling price is 1679802.053 in currency. This amount is approximately advantageous for the company as their revenue can be useful to increase the profits of the company. The minimum selling price of the range of used cars is obtained to be 49000 and the maximum selling price is 35000000. This range of selling prices is very useful for the increase of profits for the company (Das Adhikary et al, 2022). The use of predictive analytics in the form of regression is being used to evaluate the influence of the selling price variables on the kilometers which is used to evaluate the strategies to increase profits for the business.

Regression Analysis (Predictive)

Figure 3: Regression Analysis (Predictive)

(Source: self-created in MS EXCEL)

With the help of visualisation tools, it is obtained that diesel is the preferred fuel and in the pivot model, the curve represents the rise of the trend of automated diesel cars of Mercedes Benz. The colour that is obtained in demand is white and the kind of ownership in demand is for the first hand. The used cars that are in most demand are those which are bought first-hand.

Pivot data table for understanding the descriptive model

Figure 4: Pivot data table for understanding the descriptive model

(Source: self-created in MS EXCEL)

The pivot table has evaluated that the highest count of cars is for the Diesel and automatic type which is about 498. This denotes that the focus of the company should be on the diesel-powered cars which are being-run automatically. The next variable which has a notable influence is the volume of the fuel tanks and the kilometers travelled (Venkatasubbu and Ganesh, 2019). It is observed with the help of predictive analysis in MS excel where the use of regression analysis is being used to evaluate the influence of the kilometers travelled to the capacity of the fuel tank. The engine size residual plot denotes that the focus of the company should be more on the used cars where the size of the engines is around 1902 cc.

Regression Analysis (predictive analysis)

Figure 5: Regression Analysis (predictive analysis)

(Source: self-created in MS EXCEL)

With the help of different curves and strategies of statistics, the data set of the used cars is being analysed to bring insights into the demand for used cars. The intercept obtained from the regression analysis is used to prejudice the trends of the data and the intercept obtained is 44215.47 (Cleff, 2019). The company has evaluated much essential information such as the brand of the car, the colour of the car, the location where the company should focus, the mode of working of the car and the kind of ownership of the cars that are being preferred by the people and that should be kept in focus by the people. Another essential variable is the year which is to be evaluated using descriptive statistics to find the descriptive parameter of the variables.

Descriptive analysis of year

Figure 6: Descriptive analysis of year

(Source: self-created in MS EXCEL)

The descriptive statistics are being used to evaluate the central tendencies and variances of the variable year. The observed information is that the average year of manufacture of Mercedes Benz is 2016, which is to be kept in focus. The minimum year is 1988 and the maximum year is 2022, this denotes that the demand for the model of Mercedes Benz is highly manufactured in the year 2016, which is a valuable insight for the company.


The data set which contains bulk data for the used cars are being evaluated and analysed using efficient statistical methods. The variables which have influence on the selling price of the used cars are being identified and then the influence is being evaluated. It is obtained that the company should keep the focus on the model of Mercedes Benz which are of the first hand when they were bought and whose colour is white and the year to keep in concern is 2016. The location which can increase the flow of revenue for the company is identified as Delhi and the mode of working that is kept in focus is diesel powered automated cars.



  • Cleff, T., 2019. Applied statistics and multivariate data analysis for business and economics (pp. 433-441). Available from:[Available at 10.05.2023]


  • Venkatasubbu, P. and Ganesh, M., 2019. Used Cars Price Prediction using Supervised Learning Techniques. Int. J. Eng. Adv. Technol.(IJEAT)9(1S3).
  • Das Adhikary, D.R., Sahu, R. and Pragyna Panda, S., 2022. Prediction of used car prices using machine learning. In Biologically Inspired Techniques in Many Criteria Decision Making: Proceedings of BITMDM 2021 (pp. 131-140). Singapore: Springer Nature Singapore.
  • Divya, K. and Girisha, G., 2021. Autonomous car data collection and analysis. Int. Journal of Scientific Research & Engineering Trends7(3).


  • Athanasopoulou, A., de Reuver, M., Nikou, S. and Bouwman, H., 2019. What technology enabled services impact business models in the automotive industry? Available from: [Available at 10.05.2023]


  • (2019). Here’s why India’s used-car market is touted to double in five years. Available from: [Available on 10.05.2023]
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