In this project, we would analyze the advertising budget dataset of XYZ firm in Python and explore how sales of products vary with different modes of advertisement in Python.
This Project requires knowledge of basic Python Programming and to be familiar with the working environment i.e., Jupyter Notebook.
It also requires pre-installation of the certain library in the Jupyter Notebook:
1. pandas library
2. NumPy library
3. sci-kit learn library
Linear Regression is basically a linear approach of modeling one or more relationships i.e., with a single target variable we can have multiple input parameters. It describes how and to which parameters we can obtain a linear relationship between the model. We all have heard about the linear equations in our 7th or 8th standard and might have come across the slope-intercept equation:
y = m*x+c
This equation explains to us that for multiple parameters we can have multiple values of x and y with m being the slope and c as intercept and obtain a linear relationship between x and y. Similar is the case and also a bit more advanced as here we not only find values with equations but also try to solve real-life problems and implement them on the model itself to observe how accurately your model is able to predict the linearity within the model from future/ unforeseen values and hence is able to predict the future outcomes.
Here we will observe, how machine learning behaves on such possible scenarios and would also try to improve them further.
Submitted by Abhishek Kumar Singh (Abhishek)
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