TECHNICAL FIELD [0001] The present disclosure relates to the field of automotive data. More particularly the present disclosure relates to a system and method for quality marking of automotive data related to one or more automotive or automobiles. BACKGROUND [0002] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art. [0003] A tectonic shift is taking place in the auto industry. Changes in technology, consumer behavior, and emerging markets are disrupting traditional modes of operation. Today’s vehicles are more sophisticated and complex. Electronics, software, and online connectivity all pose new service, security, and privacy challenges and opportunities. In addition, the industry must contend with changes in the way cars are used, as well as how and where they are marketed. Typically, there can be 500+ data signals that are generated from a connected vehicle. Automotive data signals collected from different original equipment manufacturers (OEM), different Trade-Related Investment Measures (TRIM) within same OEMs and same make or model in different countries, varies significantly. Hence, there is a difference in the data provided by each source pertaining to number of collected signals, format and frequency of distributed data. Based on the industry and associated use cases, data consumers are interested to consume specific data signals at a particular frequency. Based on the source of data in vehicle, the data signals can be broadly be Telematics, Body Control, ADAS, Diagnostics, In-Vehicle Infotainment. Different categories of data have different value. A certain set of data signals can be used across industries and use cases, which makes them extremely valuable for the enterprises engaged in the business of data collection and distribution. Presence of these high value signals made available at a configurable frequency is an important factor in determining the value of data. [0004] However, as of today there is no method for creating an Automotive Data Quality Marker (ADQM) for determining/evaluating the quality such as quality metric of automotive data such as Telematics, Body Control, ADAS, Diagnostics, and In-Vehicle Infotainment) generated by a vehicle (i.e., data source) through the incorporation of a machine learning algorithm. As a result, the data collection, handling, analysis, processing of such huge data becomes impossible to handle and erroneous results often occur. Further, there is no standardization or common taxonomy around the signals that shall be generated by connected car and at what frequency they shall be generated. The type of signals present in the data received is as important as the volume of data shared by the vehicle manufacturer. A quantitative mechanism to derive the quality of vehicle data along with the supported frequency is an important step towards seamless and transparent data value prediction. Currently, there is no standard set of high value signals in the automotive segment and the knowledge pertaining to these signals is scattered across the industry. Additionally, to derive useful insights of interest from the data, data consumers not only need specific data signals but shall need sufficient and balanced data samples at specific frequency, which are representative of large data population. Overall, quality of automotive data signals is a factor of presence of high value data signals, frequency of data samples and number of data samples. But none of the prior art discloses any method that are able to derive useful insights from the huge amount of data. [0005] There is therefore, a need in the art to provide a system and a method that can overcome the shortcomings of the existing prior art and leverage machine learning /artificial intelligence for assessing the quality of vehicular data. OBJECTS OF THE PRESENT DISCLOSURE [0006] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below. [0007] It is an object of the present disclosure to provide a baseline data metric for automotive data collection and distribution that has advantages around technical ease of use, adoption and business value. [0008] It is an object of the present disclosure to enable scaling up of data both in volumes and dimensionality with 5G connected vehicles. [0009] It is an object of the present disclosure to train a machine learning based model to handle change in data patterns such as addition of signals, change in formats etc., making it a scalable method for calculating automotive data quality. [0010] It is an object of the present disclosure to provide quantification of data quality to enables easy valuation of use cases. [0011] It is an object of the present disclosure to provide a defined criteria for data quality to give a direction to data providers to focus on the monitoring and collection of data points that are relevant for industry use. [0012] It is an object of the present disclosure to facilitate identification of the important data signals, frequency of collection and data quality before processing the data helps in optimizing the resources (storage, processing and networks), thus giving a cost advantage. SUMMARY [0013] The present disclosure relates to the field of automotive data. More particularly the present disclosure relates to a system and method for quality marking of automotive data related to one or more automotive or automobiles. [0014] A system for data quality marking (ADQM) of auto-motive data associated with one or more automobiles. The proposed system may include a plurality of Distributed Source Systems associated with a plurality of automotive data sources; a plurality of Distributed Storage Systems to store a set of data packets pertaining to any or a combination of numeric data generated in a vehicle during a trip and image dataset for example images captured through a dashboard camera during the trip; a processor; a data quality module coupled to the processor. The data quality module may be configured to receive the set of data packets from the distributed storage systems, the set of data packets may be received at a specific frequency and in specific quantity/numbers. The system may further include a Machine learning (ML) engine coupled to the processor, the ML engine configured to: extract a first set of attributes from the set of data packets received from the first computing devices associated with the plurality of automotive data sources, the first set of attributes pertains to automotive numeric data set; extract a second set of attributes from the set of data packets received from the first computing devices associated with the plurality of automotive data sources, the second set of attributes pertain to automotive image data set; and based on the first and the second set of attributes extracted, the ML engine may further determine an average score pertaining to quality of data in the set of data packets received, wherein the average score comprises the automotive data quality marker (ADQM); and then classify, based on the ADQM, the set of data packets related to the one or more automobiles among one or more pre-defined categories for quality marking. [0015] Another aspect of the present disclosure pertains to a method facilitating auto generating automotive data quality marker. The method may include the step of receiving by a data quality module coupled to a processor, a set of data packets from a plurality of distributed storage systems associated with a plurality of automotive data sources, the set of data packets may be received at a specific frequency and in specific quantity/numbers; extracting, by an ML engine coupled to the processor, a first set of attributes from the set of data packets received from a plurality of first computing devices associated with the plurality of automotive data sources, the first set of attributes pertain to automotive numeric data set; extracting, by the ML engine coupled to the processor, a second set of attributes from the set of data packets received from the first computing devices associated with the plurality of automotive data sources, the second set of attributes pertain to automotive image data set; and based on the first and the second set of attributes extracted, determining, by the ML engine, an average score pertaining to quality of data in the set of data packets received, wherein the average score comprises the automotive data quality marker (ADQM) and then classifying, based on the ADQM, the set of data packets related to the one or more automobiles among one or more pre-defined categories for quality marking. [0016] Various objects, features, aspects and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components. BRIEF DESCRIPTION OF DRAWINGS [0017] The accompanying drawings are included to provide a further understanding of the present disclosure, and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure. The diagrams are for illustration only, which thus is not a limitation of the present disclosure. [0018] In the figures, similar components and/or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label with a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label. [0019] FIG. 1 illustrates a network architecture of system for data quality marking (ADQM) of auto-motive data associated with one or more automobiles, according to an example embodiment of the present disclosure. [0020] FIG. 2 illustrates a representation of a data quality module for determining ADQM in a network, according to an example embodiment of the present disclosure. [0021] FIG. 3 illustrates a representation of a proposed method for data quality marking (ADQM) of auto-motive data associated with one or more automobiles, according to an example embodiment of the present disclosure. [0022] FIG. 4 illustrates a representation of an overall system configuration, according to an example embodiment of the present disclosure. [0023] FIG. 5A illustrates an example representation of a block diagram highlighting model training for trip Numeric data Quality Marker (NDQM) in accordance with an embodiment of the present disclosure. [0024] FIG. 5B illustrates an example representation of a block diagram highlighting a convolutional neural network (CNN) model in accordance with an embodiment of the present disclosure. [0025] FIG. 6 illustrates a computer system in which or with which embodiments of the present invention can be utilized according to example embodiments of the present disclosure. DETAILED DESCRIPTION [0026] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims. [0027] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details. [0028] The present disclosure relates to the field of automotive data. More particularly the present disclosure relates to a system and method for quality marking of automotive data related to one or more automotive or automobiles. [0029] The present disclosure elaborates upon a system for data quality marking (ADQM) of auto-motive data associated with one or more automobiles. The system includes one or more first mobile computing devices associated with the one or more automobiles. A data quality module, having a processor, operatively configured with the one or more first mobile computing devices, and configured to execute a set of instructions, stored in a memory, which, on execution, causes the system to receive, from the one or more first mobile computing devices, a set of data packets pertaining to an information related to the one or more automobiles, wherein the information comprises a combination of numeric data and image data. Extract, based on the set of data packets, a fist set of attributes and a second set of attributes, wherein the first set of attributes pertains to numeric data and the second set of attributes pertains to image data. Generate, based on the set of first attributes and the set of second attributes, an average score for the information related to the one or more automobiles. Classify, based on the average score, the information related to the one or more automobiles among one or more pre-defined categories for quality marking. [0030] In an embodiment, the information can include but not limited to any or combination of telematics, Body Control, ADAS, Diagnostics, an in-vehicle infotainment. [0031] In an embodiment, the system can combine supervised and unsupervised learning for predicting numeric data quality Numeric data Quality Marker (NDQM), wherein the system further combines a convolution neural network (CNN) model for predicting image data quality by using Image Data Quality Marker (IDQM). [0032] In an embodiment, the system can combine the Numeric Data Quality Marker (NDQM) and Image Data Quality Marker (IDQM) to calculate a Trip Data Quality Marker (TDQM) based on a predefined set of parameters. [0033] In an embodiment, the predefined set of parameters can include any or combination of Data Type (DTQ), Key Data Features, Data Mode (DMQ), Data Frequency (DFQ), Data Validity (DVQ), and Data Skewness (DSQ) and wherein the set of predefined parameters comprises standardizing each individual parameter through rescaling of each individual quality markers and applying weighted values to each individual parameter. [0034] In an embodiment, the system can be configured to use CNN modes for deriving the data quality marker for the image data, wherein the quality for numeric trip data is predicted using Linear regression, logistic regression, Naive Bayes, kNN, random forest, Principle Component Analysis (PCA) and a combination thereof. [0035] In an embodiment, the data type (DTQ) can include personalized data and aggregated data of a user associated with the automotive. [0036] In an embodiment, the key data features can include automotive data pertaining to an overall trip data, vehicle profile, driver profile, driving pattern, safety data, media data, critical events, vehicle health, driver behavior and a combination thereof. [0037] In an embodiment, the data mode can collect data and share it in real-time, batch, historical or a combination thereof. [0038] In an embodiment, the data validity can comprise data boundary checks to remove out of bound values, signal correlation and dependency. [0039] In an embodiment, the data skewness can include balanced data to draw inferences from trip duration, vehicle types, driver Profile, location and a combination thereof. [0040] A method facilitating auto generating automotive data quality marker, the method includes receiving, by a processor, a set of data packets from a one or more first mobile computing devices associated with one or more automobiles, wherein the set of data packets comprises a combination of a numeric data and an image data. Extracting, by the processor, a fist set of attributes and a second set of attributes based on the set of data packets, wherein the first set of attributes pertains to numeric data and the second set of attributes pertains to image data. Generating, based on the set of first attributes and the set of second attributes, an average score for the information related to the one or more automobiles. Classifying, based on the average score, the information related to the one or more automobiles among one or more pre-defined categories for quality marking. [0041] FIG. 1 illustrates a network architecture in which or with which data quality module of the present disclosure can be implemented, according to an example embodiment of the present disclosure [0042] For simplicity and illustrative purposes, the present disclosure is described by referring mainly to examples thereof. The examples of the present disclosure described herein can be used together in different combinations. In the following description, details are set forth in order to provide an understanding of the present disclosure. It will be readily apparent however, that the present disclosure can be practiced without limitation to all these details. Also, throughout the present disclosure, the terms “a” and “an” are intended to denote at least one of a particular element. As used herein, the term “includes” means includes but not limited to, the term “including” means including but not limited to. The term “based on” means based at least in part on. [0043] The present disclosure describes systems and methods for determining Automotive Data Quality Marker. The term “Automotive Data Quality Marker (ASQM)” is a universal marker to measure quality of automotive signals generated from a plurality of data sources. Automotive signals quality is an outcome of a plurality of factors ranging from critical data signals to the cost of collecting and distributing that data (sampling and sharing frequency). [0044] The present invention provides a robust and effective solution to an entity or an organization for creating and standardizing an Automotive Data Quality Marker (ADQM) to determine/evaluate/predict the quality of automotive data such as Telematics, Body Control, ADAS, Diagnostics, Dashcams, and In-Vehicle Infotainment but not limited to the like generated by the vehicle (i.e., data source) using a machine learning (ML). The system comprises an amalgamation of machine learning algorithms to determine ADQM for a particular dataset. Data pertaining to vehicles is huge and repetitive. Re-training of the model for improved accuracy is a requirement as automotive data can be augmented with additional signals and data received and stored as trip objects on a regular basis. [0045] FIG. 1 illustrates a network architecture of system for data quality marking (ADQM) of auto-motive data associated with one or more automobiles or automotives, according to an example embodiment of the present disclosure. [0046] As illustrated, an auto generating automotive data quality marker system (100), hereinafter referred to as system (100), amongst other components, includes a data quality module (110) (also referred to as data quality module 110 hereinafter) having a processor for collecting and analyzing a set of data packets from one or more first computing devices (104) associated with plurality of automotive (102-1, 102,...102-N) (collectively referred to as automotive (102) and individually referred to as automotive (102) to be stored in one or more second computing devices (108) associated with an entity. In an embodiment, the set of data packets can correspond to automotive data signals from the plurality of automotive (102) but not limited to it and can include automotive data signals corresponding to telematics, Body Control, ADAS, Diagnostics, in-Vehicle infotainment and the like. [0047] In an example embodiment, the one or more first computing devices (104) can include a plurality of Distributed Source Systems. And the one or more second computing devices (108) can include a plurality of Distributed Storage Systems. [0048] In an example embodiment, the one or more first computing devices (104) can include a plurality of Distributed Source Systems. And the one or more second computing devices (108) can include a plurality of Distributed Storage Systems. [0049] In an example embodiment, the set of data packets can be stored as trip objects and the trip data can be collected from any or combination of internal and external vehicle sensors and the first computing device (104) in at least two format such as numerical data stored as trip files and a set of image files captured during the trip but not limited to the like. [0050] The data quality module (110) can be coupled to a centralized server (112). The data quality module (110) can also be operatively coupled to one or more first computing devices (104) and one or more second computing devices (108) through a network (106). [0051] In an example embodiment, the data quality module (110) can receive the set of data packets from the first computing devices (104) associated with the plurality of automotive (102). The set of data packets can pertain to numeric data, for example signal data/values generated in the vehicle during a trip, image dataset for example images captured through dashboard camera during the trip but not limited to the like. The data quality module (110) can extract a first and a second set of attributes from the set of data packets received from the first computing devices (104) associated with the plurality of automotive (102). The first set of attributes can pertain to automotive numeric data set while the second set of attributes pertain to automotive image data set. Based on the first and the second set of attributes extracted, the ADQM can be using machine learning techniques. In a way of example and not as a limitation, supervised and unsupervised learning can be combined for predicting numeric data quality Numeric data Quality Marker (NDQM) and convolution neural network (CNN) model for predicting image data quality by using Image Data Quality Marker (IDQM). An overall Trip Data Quality Marker (TDQM) can be calculated by multiplying weights assigned to numeric data and image data) with Numeric Data Quality Marker (NDQM) and Image Data Quality Marker (IDQM) respectively and summing them together. [0052] In an example embodiment, an overall ADQM can be calculated using an average function but not limited to it, wherein the automotive data quality can be categorized/classified/quantified and can be interpreted as poor, average, good, and excellent based on the calculated ADQM values/range. For example, for ADQM<=1 can be assigned as poor, 1