Especially in the field of trade and e-commerce Sales prediction is a general business use case for machine learning. As per today’s trend machine learning is perceiving an immense growth in the area of sales prediction our resource team are always equipped with the sufficient knowledge and the ethics of machine leaning. We achieve your academic goals by offering an extensive support by crafting original research proposal work. As we are professionals in this field for more than 18+ years we enhance the quality of our work and confidentiality throughout our process. In our work inventory management, optimizing marketing approaches and enhancing customer experience will aids to forecast the sales.
Here we had given step-by-step guidance to constructing a sales prediction project utilizing machine learning:
State the aim, for example: “On the basis of previous data we forecast the future sales for the next month for every product in our inventory”.
We consider the patterns and drivers behind sales, business that make well-versed decisions, optimize operations, and enhance possibility. In our work Machine learning provides powerful tools to attain this, but it is critical to support the project closely with business goals and field knowledge.
On time delivery and work confidentiality is our main principle. Get your survey paper upon your request that will be done to the finest according to the rues and standards. Trust us we also deliver tailored research work with a good source of proposal writing service where brief discussion will be given.
Dissertation Ideas and Topics that we have done are shared below, get inspired by our work contact us if you are in need of customized paper writing work. We combine many techniques and algorithms to get the proper output, our machine learning professionals plays a vital role in this.
Machine learning algorithms, Machine learning, Prediction algorithms, Naive Bayes methods, Food products, Informatics
The machine learning method can be used in our paper predict the parametric evaluation and conclusion matrix. Our paper identifies the complete sales with high accuracy and other extra classifications of features are also predicted and we can also predict the location wise sales, super market sales and other category of sales. The methods we have to use for prediction are KNN, Naive Bayes and some of them were utilized for prediction.
Drug sales forcasting, random forest, support vector machine, XGBoost
Our paper uses ML methods for predicting future sales such as Logistic regression, Random Forest, Support Vector Machine and XGBoost and associates this with various methods on some mostly utilized drugs. The dataset we utilized were consisted of drug sales from different drugs namely antipyretics, antihistamines, etc. After the data has been preprocessed the four ML methods were utilized to predict the sales. XGBoost will predict the better result.
Regression, sales, supervised learning
Regression model can be used to predict the future sales and it also utilized to construct a mathematical tool. Common regression methods are linear regression, polynomial regression, ride regression, Lasso regression, ElasticNet regression, SVM regression and Decision Tree regression. Cross validation method can be utilized to confirm the generalization ability of the model. Adaboost regressor, Bayesian bidge and Ridge regression have given the best performance. Our paper uses Adaboost at last to verify the data.
Deep Learning, Sales prediction, LSTM, IARIMA, Neural Networks
Our paper utilizes the use of historical data in online trade market to construct the framework anticipate sales. As stated by the quality of various data three sorts of techniques were used namely Incentive-Auto-Regressive-Integrated-Moving-Average (I-ARIMA), LSTM and ANN. These 3 methods can handle accuracy requirements and different data types. Our LSTM method gives the general implementation over others.
Sales forecasting, ARIMA, Prophet, Retail prediction
To predict the analytics of sales our paper uses ML methods. The dataset we used are Rossmann chain of drug stores and the feature engineering helps the significance of different features and by clan the data by managing outliers and missing data. We selected the ARIMA Model, Facebook’s Prophet Model and XGBoost Mode to compare the model. Our ARIMA model gives the best performance when compared to others.
Linear Regression, Advertisement, Television, Radio, Prediction
Our study concentrates to identify the correlation between sales and advertisement and our paper helps to find which advertisement is appropriate to improve sales. The use ML methods are the significant portion for designing business and also we have to concern the purchase pattern of customers. Linear regression can be used to predict the most accurate to produce more sales.
House Sales Price Prediction, Gradient Boosting, Extra-Trees
The machine learning methods can be used to predict the house price and our study offers a performance of different regression methods like Linear Regression, Random Forest, Gradient Boosting and Extra-Trees for house sales price prediction. We find the most optimal method to accurately predict the household prices. The most important features can be done by utilizing the feature extraction method. Extra-Trees method can performs best when compared to other regression methods.
Classification, Black Friday.
Machine Learning method can be utilized to judge and predict the result exactly. Predictive methods are utilized to control the most possible result based on the data present. Our work aids to improve and design the predictor model that will give support to sales organization at the time of black Friday. The improved method to implement earlier will test with various classification techniques. Random-forest regression based method can be utilized to predict black Friday sales.
GBT Regression, Random Forest Regression
Machine Learning methods can predict the automobile prices based on several features. Many individual qualities can be used to predict accurate result. Our method uses a dataset that contain several features that can affect car prices. Our study uses Linear Regression, GBT Regression and Random Forest Regression to evaluate second-hand car prices. The performance of the method can compared to the best fit dataset.
Data visualization, Forecasting
We have to predict the future sales on mega mart by utilizing various machine learning methods. Our paper uses the methods like Linear Regression, Decision Tree, Random Forest, Ridge Regression and XGBoost method to predict the opening sale. XGBoost outperforms the better prediction rate. We predict the sales on Mega mart can detect the different patterns that can be convert to ensure success in business.
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