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Video games are a multibillion-dollar industry that has been around for a long time. The global PC gaming market is expected... moreVideo games are a multibillion-dollar industry that has been around for a long time. The global PC gaming market is expected to earn about '37' billion dollars in revenue in 2020, while the mobile gaming market is expected to generate over '77' billion dollars. The Project aims to predict a video game's total global sales figures.The dataset contains a list of video games with sales greater than 100,000 copies.'r2_score' has been used to check the model's performance.
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September 23, 2021 - MODEL_posted_by
Manish Arram,
1,158 views, 1 like
PRE-OWNED-CAR-PRICE-PREDICTION
A car price prediction has been a high interest research area, as it requires noticeable... morePRE-OWNED-CAR-PRICE-PREDICTION
A car price prediction has been a high interest research area, as it requires noticeable effort and knowledge of the field expert. Considerable number of distinct attributes are examined for the reliable and accurate prediction.
To build a model for predicting the price of used cars we use the regression algorithms.
Respective performances of different algorithms were then compared to find one that best suits the available data set. The final prediction model was integrated into python application. Furthermore, the model is evaluated using test data with the suitable Performance Metrics.
Introduction
Price prediction of a car especially when the vehicle is used or pre-owned and not coming direct from the factory, is both a critical and important task. With increase in demand for used cars more and more vehicle buyers are finding alternatives of buying new cars.
There is a need of accurate price prediction mechanism for the used cars. Prediction techniques of machine learning can be helpful in this regard EXISTING SYSTEM
In the existing system, to predict the price of vehicles, a lot of machine learning algorithms were widely used. The major drawback of this existing system is they need more attributes in order to predict the vehicle price. More comparison techniques must be used to get the result more efficiently. It is highly complicated to get sufficient data sets that were spread widely all over the world. The datasets can be collected only through online. But not on the offline mode. It is not possible for everyone to collect the data sets through online mode particularly in rural areas. The data sets will not have about the vehicles which were not used for long time and also the traditional model vehicles may or may not be included in the data sets.so it is difficult to predict accurate price. PROPOSED SYSTEM
Based on the varying features and factors, and also with the help of experts knowledge the vehicle price prediction has been done accurately. The most necessity ingredient for prediction is Category of vehicle, Rating of vehicle, period usage of vehicle, Kilometers Driven of vehicle, The Fuel Type & Transmission Type of vehicle. Different features like Which Owner, Insurance Left or Expired, Insurance Type will also influence the vehicle price. In this project, we applied different methods and techniques in order to achieve higher R-square of the used vehicle price prediction. less
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May 1, 2021 - MODEL_posted_by
Nitara Bobal,
841 views, 3 likes
We use machine learning techniques to group retail store customers together, based on the different types of products that... moreWe use machine learning techniques to group retail store customers together, based on the different types of products that they buy, and predict what kind of product a customer will buy in the future.
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April 20, 2021 - MODEL_posted_by
Tarun Reddy,
900 views, 1 like
Sales analysis is mining your data to evaluate the performance of your sales team against its goals. It provides insights... moreSales analysis is mining your data to evaluate the performance of your sales team against its goals. It provides insights about the top-performing and underperforming products/services, the problems in selling and market opportunities, sales forecasting, and sales activities that generate revenue. In this usecase the dataset has 8523 rows of 12 variables.Item_Identifier- Unique product ID, Item_Weight- Weight of the product, Item_Fat_Content - Whether the product is low fat or not, Item_Visibility - The % of the total display area of all products in a store allocated to the particular product, Item_Type - The category to which the product belongs, Item_MRP - Maximum Retail Price (list price) of the product, Outlet_Identifier - Unique store ID, Outlet_Establishment_Year- The year in which store was established, Outlet_Size - The size of the store in terms of ground area covered, Outlet_Location_Type- The type of city in which the store is located, Outlet_Type- Whether the outlet is just a grocery store or some sort of supermarket, Item_Outlet_Sales - Sales of the product in the particular store. This is the outcome variable to be predicted. less
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