CAR PRICE PREDICTION SYSTEM

Abstract

Disclosed herein is a system (100) for predicting car prices, which comprises a user device (102) configured to collect historical car data, a microprocessor (108) communicably connected to the user device (102) via a communication network (104) and configured to process data, wherein the microprocessor (108) further comprises a data input module (112) configured to receive car data from the user device (102), a data processing module (114) configured to clean and process the collected data, including handling categorical variables and outliers, a visualization module (116) configured to generate visual representations of the processed data to facilitate better understanding and analysis, a prediction module (122) configured to predict car prices based on a trained linear regression model using pre-defined parameters, and an output module (124) configured to display the predicted car prices and related information on a display screen (110) integrated with the user device (102).

Specification

Description:FIELD OF DISCLOSURE
[0001] The present disclosure generally relates to the field of predictive modeling, more specifically, relates to a car price prediction system that utilizes a linear regression model based on machine learning techniques.
BACKGROUND OF THE DISCLOSURE
[0002] Predictive modeling is a statistical method utilized to anticipate future results by analysing historical data and recognizing patterns. Through the application of algorithms and mathematical frameworks, predictive modeling allows users to examine trends and make informed decisions in diverse domains. This process generally entails identifying significant independent variables that affect a specific dependent variable, followed by the creation and validation of models capable of providing accurate predictions.
[0003] Predictive modeling plays a crucial role in the automotive industry, particularly in the domain of car valuation. By leveraging historical sales data, market trends, and various independent variables, predictive models can generate accurate appraisals of vehicle prices.
[0004] Traditional methods of car valuation often rely on subjective assessments or manual evaluations, which can result in inconsistencies and inaccuracies. With the rapid advancement of technology, there is a growing need for automated systems that can provide precise car price predictions based on various influencing factors.
[0005] Machine learning techniques, particularly linear regression models, have emerged as effective tools for predictive analysis in various fields, including finance, real estate, and e-commerce. Linear regression offers a straightforward approach to modeling the relationship between independent variables and a dependent variable. By analyzing historical car data, these models can identify patterns and trends that inform pricing strategies.
[0006] However, many existing car price prediction systems suffer from significant drawbacks. They often rely on simple linear regression techniques that may not accurately capture the complexities of various factors affecting car prices, leading to unreliable predictions, especially in dynamic markets where prices fluctuate due to economic changes and consumer behaviour.
[0007] Additionally, conventional systems frequently struggle to handle large volumes of diverse data, which can result in overlooked relationships among important independent variables. Many prior art systems lack mechanisms for continuous learning or updating, rendering them static and unresponsive to new data inputs or shifting market conditions, thus producing outdated predictions. Furthermore, these models typically focus on linear relationships while neglecting potential non-linear interactions that could greatly impact pricing accuracy. They also often lack robust visualization tools, making it challenging for users to understand data and prediction results. Additionally, a heavy reliance on historical data without considering real-time market trends can skew predictions in volatile environments. Finally, some systems fail to provide intuitive user interfaces, limiting accessibility and practical application for non-technical users. Overall, these limitations hinder the effectiveness and usability of current car price prediction systems.
[0008] The present invention overcomes the limitations of the prior art by providing a robust and accurate car price prediction system that leverages machine learning techniques in conjunction with a linear regression model to predict car prices. This approach allows for the effective analysis of large datasets, leading to improved prediction accuracy compared to traditional methods. Furthermore, the present invention facilitates real-time updates to the predictive model as new data is collected, ensuring that the car price predictions remain relevant and reflective of current market conditions. This dynamic updating capability enhances the system's adaptability and responsiveness to changes in consumer preferences and market trends.
[0009] Unlike prior art systems, the present invention incorporates advanced visualization tools that facilitate a clearer understanding of data trends and prediction results. This empowers users to make informed decisions based on visual insights rather than relying solely on numerical outputs. By incorporating a user-friendly interface and a robust database, the present invention aims to enhance the accuracy and reliability of car pricing predictions, ultimately benefiting both buyers and sellers in the automotive market.
[0010] Thus, in light of the above-stated discussion, there exists a need for a car price prediction system.
SUMMARY OF THE DISCLOSURE
[0011] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0012] According to illustrative embodiments, the present disclosure focuses on a car price prediction system which overcomes the above-mentioned disadvantages or provide the users with a useful or commercial choice.
[0013] The present disclosure solves all the above major limitations of a car price prediction system.
[0014] An objective of the present disclosure is to develop a car price prediction system that leverages advanced machine learning techniques in conjunction with a linear regression model.
[0015] Another objective of the present disclosure is to provide a system which offers valuable insights and accurate car price predictions that assist buyers, sellers, and other stakeholders in making informed decisions in the automotive market.
[0016] Another objective of the present disclosure is to provide a system that can effectively analyse and integrate large volumes of diverse data, capturing complex relationships among various independent variables that influence car prices.
[0017] Another objective of the present disclosure is to provide a system that continuously learns and adapts based on new data inputs, ensuring the predictions remain relevant and reflective of current market conditions.
[0018] Another objective of the present disclosure is to provide a system with robust visualization features that facilitate a clearer understanding of data trends and prediction results, empowering users to make informed decisions based on visual insights.
[0019] Yet another objective of the present disclosure is to incorporate a user-friendly interface that facilitates seamless interaction with the car price prediction system, allowing users to easily input data, view predictions, and understand analytical results.
[0020] In light of the above, in one aspect of the present disclosure, a system for predicting car prices is disclosed herein. The system comprises a user device configured to collect historical car data. The system also includes a microprocessor communicably connected to the user device via a communication network and configured to process data, wherein the microprocessor further comprises a data input module configured to receive car data from the user device, a data processing module configured to clean and process the collected data, including handling categorical variables and outliers, a visualization module configured to generate visual representations of the processed data to facilitate better understanding and analysis, a prediction module configured to predict car prices based on a trained linear regression model using pre-defined parameters, and an output module configured to display the predicted car prices and related information on a display screen integrated with the user device.
[0021] In one embodiment, the user device is configured to collect historical car data based on independent variables, including but not limited to car make, model, year, mileage, engine size, fuel type, transmission, and condition.
[0022] In one embodiment, the visualization module is configured to generate graphs, charts, and heat maps representing relationships between various car attributes and their impact on predicted prices, helping users to identify patterns, trends, and anomalies.
[0023] In one embodiment, the system further comprises a training and testing module configured to split the processed data into training and testing datasets, and train the linear regression model on the training data.
[0024] In one embodiment, the training and testing module further includes mechanisms for iterative refinement based on feedback from the testing dataset to improve model performance.
[0025] In one embodiment, the system further comprises a dynamic update module configured to continuously update the trained linear regression model based on new data inputs to improve future prediction accuracy.
[0026] In one embodiment, the prediction module utilizes pre-trained linear regression models stored in a cloud database trained on historical car data, enabling the prediction of car prices based on previously trained models.
[0027] In one embodiment, the prediction module predicts car prices using pre-defined parameters including, but not limited to, sales, income, age, and product price.
[0028] In one embodiment, the output module is further configured to display additional information such as error margins, confidence intervals and predicted vs. actual price comparison.
[0029] In light of the above, in another aspect of the present disclosure, a method for predicting car prices is disclosed herein. The method comprises collecting historical car data via a user device. The method also includes processing data via a microprocessor comprising of several modules. The method also includes receiving car data from the user device via a data input module. The method also includes cleaning and processing the collected data, including handling categorical variables and outliers via a data processing module. The method also includes generating visual representations of the processed data to facilitate better understanding and analysis via a visualization module. The method also includes predicting car prices based on a trained linear regression model using pre-defined parameters via a prediction module. The method also includes displaying the predicted car prices and related information on a display screen integrated with the user device via an output module.
[0030] These and other advantages will be apparent from the present application of the embodiments described herein.
[0031] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0032] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0033] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0034] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0035] FIG. 1 illustrates a block diagram of a car price prediction system, in accordance with an exemplary embodiment of the present disclosure;
[0036] FIG. 2 illustrates a schematic of the car price prediction system, depicting the interactions among its various components, in accordance with an exemplary embodiment of the present disclosure; and
[0037] FIG. 3 illustrates a flowchart of a method, outlining the sequential steps for predicting car prices, in accordance with an exemplary embodiment of the present disclosure.
[0038] Like reference, numerals refer to like parts throughout the description of several views of the drawing.
[0039] The car price prediction system is illustrated in the accompanying drawings, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0040] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to 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 spirit and scope of the present disclosure.
[0041] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0042] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0043] The terms "a" and "an" herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0044] The terms "having", "comprising", "including", and variations thereof signify the presence of a component.
[0045] Referring now to FIG. 1 to FIG. 3 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a block diagram of a car price prediction system 100, in accordance with an exemplary embodiment of the present disclosure.
[0046] The system 100 may include a user device 102, and a microprocessor 108 further comprising a data input module 112, a data processing module 114, a prediction module 120, a visualization module 122, and an output module 124.
[0047] The user device 102 is configured to collect historical car data, which forms the foundational input for predicting car prices. The user device 102 may be a smartphone, tablet, computer, or any other network-enabled device capable of gathering and transmitting car-related data. The user device 102 facilitates a user-friendly interface, allowing users to easily enter data through text fields, which can streamline the data entry process. The user device 102 serves as the primary interface for users to interact with the system 100, facilitating the seamless collection and transmission of data necessary for accurate car price predictions.
[0048] In one embodiment of the present invention, the user device 102 is configured to collect historical car data based on independent variables, including but not limited to car make, model, year, mileage, engine size, fuel type, transmission, and condition. These independent variables are crucial for the predictive analysis as they provide essential information influencing a vehicle's valuation.
[0049] The car make and model categorize vehicles, with different brands potentially having varying market values due to their popularity. The year of manufacture significantly affects depreciation rates, as newer vehicles generally command higher prices, while older models decline in value. Mileage is crucial since lower mileage often suggests less use, positively impacting market value. Engine size, measured in liters, influences performance and fuel efficiency, while fuel type whether petrol, diesel, or electric affects operational costs and demand. Transmission type, whether manual or automatic, impacts desirability based on consumer preferences. The vehicle's overall condition, including factors such as the state of the car, maintenance history, and any previous accidents or repairs, is vital for determining market value. By collecting this array of independent variables, the user device 102 enriches the data input, enhancing the ability of the system 100 to analyze trends and generate accurate car price predictions.
[0050] In another embodiment of the present invention, the user device 102 is equipped with software applications capable of accessing online data sources, market reports, and third-party APIs to automatically collect relevant historical car data. This feature not only streamlines the data-gathering process for users but also enhances the dataset by integrating comprehensive and current information regarding market trends and car valuations.
[0051] The microprocessor 108 in the system 100 serves the role of processing data received from the user device 102 and performing the necessary computational tasks required for predicting car prices. It is communicably connected to the user device 102 via a communication network 104, preferably utilizing a wireless connection. The microprocessor 108 is configured to execute various software components and serves as the backbone of the system 100, handling a range of tasks including receiving, processing, and analysing data to predict car prices. The microprocessor 108 incorporates several integrated modules, including the data input module 112, data processing module 114, visualization module 116, prediction module 122, output module 124, among others, ensuring seamless communication and data flow throughout the system 100.
[0052] The data input module 112 is designed to receive the car data collected by the user device 102. The data input module 112 is responsible for validating the received data to ensure the incoming data is complete, accurate and error free before sending it to the data processing module 114. The data input module 112 is optimized to handle the secure transmission of large datasets, ensuring that data is transferred efficiently, providing a reliable foundation for subsequent data analysis and processing.
[0053] The data processing module 114 is a key component that prepares the raw car data for predictive analysis. It performs several tasks, including cleaning the data to remove any inconsistencies or inaccuracies and processing the collected data to ensure usability. The data processing module 114 includes handling categorical variables, which may include converting non-numeric data such as car name into a numerical form that can be utilized by the predictive models. Additionally, the module detects and manages outliers, which are data points that significantly deviate from the rest of the dataset and, if not properly addressed, could distort the results and affect the accuracy of the predictions. The data processing module 114 adjusts or removes these anomalies before passing the refined data to the prediction module 122.
[0054] The visualization module 116 generates visual representations of the processed data, enabling users to easily interpret and analyse the information. These visualizations allow users to gain insights into the underlying data, facilitating a better understanding of how different variables influence car prices and to detect any significant patterns or trends.
[0055] In one embodiment of the present invention, the visualization module 116 is configured to generate graphs, charts, and heat maps representing relationships between various car attributes and their impact on predicted prices, helping users to identify patterns, trends, and anomalies. The visualization module 116 produces various types of visual aids, such as bar graphs, line charts, and pie charts, which display trends and distributions in the dataset, making it easier for users to pinpoint key factors affecting car prices. Additionally, heat maps are used to represent correlations between different car attributes and their impact on pricing, enabling users to quickly identify areas that require attention or analysis. By making complex data more understandable, the visualization module 116 allows users to detect significant patterns, trends, and relationships between car attributes and pricing. This enhanced visual representation aids in spotting anomalies, making informed decisions, and improving the overall analysis of market dynamics.
[0056] In one embodiment of the present invention, the system 100 further comprises a training and testing module 118 configured to split the processed data into training and testing datasets, and train the linear regression model on the training data. The training dataset is used to develop and fine-tune the linear regression model by identifying patterns and relationships between various car attributes and their corresponding prices. Once the model is trained, the testing dataset is utilized to evaluate its performance and accuracy, ensuring that the model can generalize well to new, unseen data. By rigorously testing the model, the training and testing module 118 helps to prevent overfitting and ensures that the system 100 delivers reliable car price predictions based on real-world data.
[0057] In one embodiment of the present invention, the training and testing module 118 further includes mechanisms for iterative refinement based on feedback from the testing dataset to improve model performance. After the initial training of the linear regression model, the training and testing module 118 evaluates its accuracy by comparing predicted car prices with actual prices from the testing dataset. This assessment reveals the model's strengths and weaknesses, allowing the module to identify discrepancies, such as consistent underestimations or overestimations. To address these issues, the training and testing module 118 employs feedback loops to adjust model parameters. By continuously refining the model through this feedback mechanism, the training and testing module 118 ensures that the system 100 improves its accuracy over time and adapts to changing market dynamics, resulting in more reliable car price predictions.
[0058] In one embodiment of the present invention, the system 100 further comprises a dynamic update module 120 configured to continuously update the trained linear regression model based on new data inputs to improve future prediction accuracy. The dynamic update module 120 operates by regularly collecting and analysing fresh car data as it becomes available, such as recent sales, market trends, and changes in consumer preferences. As new data is introduced, the dynamic update module 120 adjusts the existing model parameters, effectively updating the linear regression model to reflect the most current information.
[0059] The prediction module 122 is responsible for estimating car prices based on a trained linear regression model. It uses pre-defined parameters to compute the estimated price of the car, providing users with data-driven insights. The prediction process involves applying the coefficients learned during the training phase of the linear regression model to the input parameters. This allows the prediction module 122 to quantify how each parameter contributes to the overall price estimation. By leveraging statistical methods and pattern recognition, the prediction module 122 can provide highly accurate price predictions, considering market fluctuations and depreciation over time. Additionally, the ability of the prediction module 122 to adapt and respond to new data inputs ensures that the price estimates remain relevant and accurate, making it an essential tool for users looking to make informed decisions in the automotive marketplace.
[0060] In one embodiment of the present invention, the prediction module 122 utilizes pre-trained linear regression models stored in a cloud database 106 trained on historical car data, enabling the prediction of car prices based on previously trained models. The historical data includes variables such as car make, model, year, mileage, engine size, fuel type, transmission, and condition. By storing pre-trained linear regression models in the cloud database 106, the system 100 can instantly access them, eliminating the need for retraining and speeding up predictions. These linear regression models, trained on large datasets, improve efficiency and accuracy by identifying patterns, trends and correlations between the independent variables and the car prices. Cloud database 106 enhances scalability, allowing for continuous data updates that refine models and improve future predictions.
[0061] In one embodiment of the present invention, the prediction module 122 predicts car prices using pre-defined parameters including, but not limited to, sales, income, age, and product price. Pre-defined parameters include sales data, which reflects market demand and consumer purchasing behaviour, providing insight into how various factors influence buying trends for different types of vehicles. Income levels indicate the purchasing power of potential buyers, allowing the prediction module 122 to gauge how economic conditions affect consumer willingness to invest in vehicles. Additionally, the prediction module 122 considers the average age of consumers, which can significantly influence preferences for specific vehicle types, features, and pricing strategies. The original product price serves as a baseline for determining value retention and depreciation trends. By analysing how the original price interacts with market conditions, the prediction module 122 can assess the vehicle's current value and forecast future price fluctuations.
[0062] In an exemplary embodiment, if a car model has a strong initial popularity, high sales, and increased demand at launch, the system 100 predicts a relatively higher resale value for that car. This strong demand indicates that consumers regard the model favourably, contributing to better value retention over time. The prediction module 122 considers these indicators, suggesting that the car's market value depreciates at a slower rate compared to less popular models, resulting in a higher estimated price in the current market.
[0063] In an exemplary embodiment, if income levels in a specific region have recently increased, the prediction module 122 may predict higher car prices in that region because consumers have more purchasing power. Conversely, if unemployment rates are rising, the system 100 may lower its predicted car prices to reflect reduced demand.
[0064] In an exemplary embodiment, if the average age of consumers is less, the system 100 predicts higher demand and prices for compact cars, which appeal to younger buyers. Conversely, if the average age of consumers is more, the system 100 predicts higher prices for larger vehicles with enhanced safety features, as older buyers may prefer more practical vehicles.
[0065] In an exemplary embodiment, if a car's original product price is significantly high, the system 100 predicts a higher resale value, provided that the car remains in good condition and possesses desirable features.
[0066] The output module 124 is responsible for displaying the predicted car prices and any related information on a display screen 110 integrated with the user device 102. It ensures that the results generated by the prediction module 122 and the visualizations from the visualization module 116 are delivered to the user in a comprehensible format. The output module 124 is designed to be responsive and compatible with various devices, ensuring that the predicted data is accessible whether the user is on a smartphone, tablet, or desktop computer. It also allows the user to interact with the data, potentially saving or exporting the results for further analysis.
[0067] In one embodiment of the present invention, the output module 124 is further configured to display additional information such as error margins, confidence intervals and predicted vs. actual price comparison. Error margins quantify variability in price predictions, helping users understand the model's reliability and associated risks in their buying or selling decisions. Confidence intervals provide a statistical range for the true market price, indicating the certainty of the model's predictions and guiding users based on their risk tolerance. Additionally, the output module 124 enables direct comparisons between predicted and actual prices, offering insights into the model's accuracy and facilitating performance assessment over time. By presenting this information, the output module 124 enhances users' understanding of market dynamics, leading to more informed decisions in the automotive marketplace.
[0068] FIG. 2 illustrates a schematic 200 of the car price prediction system 100, depicting the interactions among its various components, in accordance with an exemplary embodiment of the present disclosure.
[0069] The schematic 200 illustrates the process flow of the car price prediction system 100, detailing the interaction between various modules and components responsible for predicting car prices based on historical data. The process begins with the creation of a computational environment that establishes the necessary infrastructure required for data processing and model execution by the system 100. The computational environment is prepared by setting up the required software platforms and dependencies. Subsequently, the relevant libraries essential for data handling, machine learning, visualization, and statistical analysis are installed to ensure the system 100 functions smoothly.
[0070] Following this, the user device 102 is employed to collect historical car data, which forms the basis for the car price predictions. The collected data is passed to the data input module 112, which receives and reads the incoming data from various sources. The module validates the data, ensuring it is complete and accurate before forwarding it to the subsequent data processing stage. In the data processing module 114, the collected data undergoes cleaning and transformation, ensuring that any inconsistencies, missing values, or outliers are handled effectively. A crucial part of the data processing involves handling categorical variables, such as car brand, fuel type, and transmission type. These non-numeric variables are transformed into a numerical format suitable for use in machine learning models.
[0071] Next, the visualization module 116 is responsible for generating visual representations of the data, such as graphs, charts, and other visual aids. These visualizations allow users to gain insights into the underlying data, facilitating a better understanding of how different variables influence car prices. Once the data is processed and visualized, it is split into training and testing sets by the training and testing module 118. The training set is used to train and develop the linear regression model, while the testing set helps to assess the model's accuracy and performance on unseen data. Finally, a trained linear regression model is generated, enabling accurate predictions of car prices by the prediction module 122 based on the processed and trained data.
[0072] FIG. 3 illustrates a flowchart of a method 300, outlining the sequential steps for predicting car prices, in accordance with an exemplary embodiment of the present disclosure.
[0073] The method 300 may include, at step 302, collecting historical car data via a user device, at step 304, processing data via a microprocessor comprising of several modules, at step 306, receiving car data from the user device via a data input module, at step 308, cleaning and processing the collected data, including handling categorical variables and outliers via a data processing module, at step 310, generating visual representations of the predicted data to facilitate better understanding and analysis via a visualization module, at step 312, predicting car prices based on a trained linear regression model using pre-defined parameters via a prediction module, and at step 314, displaying the predicted car prices and related information on a display screen integrated with the user device via an output module.
[0074] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0075] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0076] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure.
[0077] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:
1. A system (100) for predicting car prices, the system (100) comprising:
a user device (102) configured to collect historical car data;
a microprocessor (108) communicably connected to the user device (102) via a communication network (104) and configured to process data, wherein the microprocessor (108) further comprises:
a data input module (112) configured to receive car data from the user device (102);
a data processing module (114) configured to clean and process the collected data, including handling categorical variables and outliers;
a visualization module (116) configured to generate visual representations of the processed data to facilitate better understanding and analysis; and
a prediction module (122) configured to predict car prices based on a trained linear regression model using pre-defined parameters; and
an output module (124) configured to display the predicted car prices and related information on a display screen (110) integrated with the user device (102).
2. The system (100) as claimed in claim 1, wherein the user device (102) is configured to collect historical car data based on independent variables, including but not limited to car make, model, year, mileage, engine size, fuel type, transmission, and condition.
3. The system (100) as claimed in claim 1, wherein the visualization module (116) is configured to generate graphs, charts, and heat maps representing relationships between various car attributes and their impact on predicted prices, helping users to identify patterns, trends, and anomalies.
4. The system (100) as claimed in claim 1, wherein the system (100) further comprises a training and testing module (118) configured to split the processed data into training and testing datasets, and train the linear regression model on the training data.
5. The system (100) as claimed in claim 1, wherein the training and testing module (118) further includes mechanisms for iterative refinement based on feedback from the testing dataset to improve model performance.
6. The system (100) as claimed in claim 1, wherein the system (100) further comprises a dynamic update module (120) configured to continuously update the trained linear regression model based on new data inputs to improve future prediction accuracy.
7. The system (100) as claimed in claim 1, wherein the prediction module (122) utilizes pre-trained linear regression models stored in a cloud database (106) trained on historical car data, enabling the prediction of car prices based on previously trained models.
8. The system (100) as claimed in claim 1, wherein the prediction module (122) predicts car prices using pre-defined parameters including, but not limited to, sales, income, age, and product price.
9. The system (100) as claimed in claim 1, wherein the output module (124) is further configured to display additional information such as error margins, confidence intervals and predicted vs. actual price comparison.
10. A method (300) for predicting car prices, the method (300) comprising:
collecting historical car data via a user device (102);
processing data via a microprocessor (108) comprising of several modules;
receiving car data from the user device (102) via a data input module (112);
cleaning and processing the collected data, including handling categorical variables and outliers via a data processing module (114);
generating visual representations of the processed data to facilitate better understanding and analysis via a visualization module (116);
predicting car prices based on a trained linear regression model using pre-defined parameters via a prediction module (122); and
displaying the predicted car prices and related information on a display screen (110) integrated with the user device (102) via an output module (124).

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