Sales Prediction using Advertisement data
Introduction
This project focuses on predicting sales using advertising data. The objective is to develop a model that can accurately estimate sales based on advertising expenditures.
Project Overview
The goal of this project is to build a machine learning model that can effectively predict sales based on advertising data from different channels such as TV, newspaper, and radio. The project involves the following steps:
Data Exploration: Analyze the advertising dataset to gain insights into the distributions, patterns, and relationships between variables. Identify any outliers or significant features within the data.
Data Preprocessing: Perform data cleaning, handle missing values, and perform any necessary data transformations such as feature scaling or normalization.
Model Development: Train a regression model using linear regression with the OLS (Ordinary Least Squares) method. Use the advertising data as predictors and the sales data as the dependent variable.
Model Evaluation: Evaluate the trained model using various metrics such as R-squared, adjusted R-squared, and the F-statistic. Perform residual analysis to assess the model’s performance and validity.
Technologies used: Exploratory Data Analysis, Data Visualization, Machine Learning, Supervised Learning Regression
GitHub Repository Link: Sales Prediction using Advertisement Data
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