Sales Prediction with Advertising
The Sales Prediction of Appliances and Advertising project aims to predict the sales of appliances based on the amount of money spent on advertising. The dataset used for this project contains information about the amount of money spent on advertising through different channels like TV, radio, and newspapers, along with the corresponding sales figures for each channel.
The goal of this project is to build a regression model that can predict the sales figure based on the advertising spend. This can help businesses to plan their advertising budgets more effectively and optimize their advertising spend to achieve better sales figures.
To achieve this, the project involves the following steps:
Data cleaning and preprocessing: This step involves cleaning the dataset to remove any missing or inconsistent data and preparing it for analysis.
Exploratory data analysis (EDA): This step involves analyzing the dataset to gain insights into the relationships between different variables, such as the relationship between advertising spend and sales.
Feature engineering: This step involves creating new features from the existing ones that can improve the performance of the model.
Model selection: This step involves selecting the best regression model that can predict the sales figure accurately based on the advertising spend.
Model evaluation and fine-tuning: This step involves evaluating the performance of the selected model and fine-tuning its parameters to improve its accuracy.
Prediction and deployment: This step involves using the trained model to make predictions on new data and deploying the model in a production environment.
Overall, the Sales Prediction of Appliances and Advertising project is an example of how machine learning can be used to solve real-world business problems and help organizations make data-driven decisions.
