In this internship, you will use simple linear regression to predict the expected sales of a Television company.

Sales expectations of any television company can be based on a variety of factors, such as market trends, past sales performance, and the business's growth plans. In order to have a real life experience you will deal with majority of these factors while setting up your model.

Sales expectations are important for a business because they provide a target for the sales team to strive towards, and they help the business plan and allocate resources. Having clear sales expectations allows the business to set goals and create a roadmap for achieving those goals. It also helps the business to track its progress and make adjustments as needed to stay on track.

For businesses, it's important to estimate how many sales are likely to happen, depending on a given advertisement strategy. By setting and tracking sales expectations, a business can improve its chances of success and avoid costly mistakes. It is your turn to do that now.

Tools you will learn in this internship:

Simple linear regression - It widely used statistical technique for modeling the relationship between a dependent variable and one or more independent variables. It assumes that the relationship between the dependent and independent variables is linear, meaning that a change in the independent variable leads to a constant change in the dependent variable. This assumption makes linear regression easy to understand and interpret, and it allows for fast and efficient calculation of the model parameters.

Numpy - This library is used in Python for scientific computing because it provides tools for working with large and complex arrays of data. It allows for efficient manipulation of the data, and it supports a wide range of mathematical operations on the data. NumPy is particularly useful for working with numerical data, such as data from scientific experiments or financial data. It is also fast and memory-efficient, making it well-suited for working with large datasets.

Pandas - This library is used in Python for data manipulation and analysis because it provides tools for working with tabular data, such as data stored in a table with rows and columns. Pandas allows for easy manipulation of the data, such as filtering, aggregation, and transformation, and it also provides tools for reading and writing data to and from a variety of formats.

Sci-kit - This library is used in Python for machine learning because it provides a wide range of algorithms for supervised and unsupervised learning, and it is built on top of NumPy, SciPy, and Matplotlib. This makes it easy to use scikit-learn within a broader data analysis workflow, and it allows for efficient manipulation of the data and powerful visualization of the results.