# Week 6 - Baseline Models and Linear Regression

### This week we will...

* get to know about the importance of baseline models
* learn about naïve forecasting
* see how linear regressions are defined
* understand the role of cost functions
* get an introduction into optimization functions

### Learning Resources

{% file src="/files/hazryXvAfXWUvjUFQCaa" %}

{% file src="/files/FhQoE0mTGwKexN4FcQDB" %}

* [DataCamp Tutorial](https://www.datacamp.com/tutorial/essentials-linear-regression-python) on Linear Regression

### Until next week you should...

* [x] Watch the videos in the section "[The problem of overfitting](https://learn.deeplearning.ai/specializations/machine-learning/lesson/sxviw/the-problem-of-overfitting)" from Week 3 of the course "Supervised Machine Learning: Regression and Classification" on DeepLearning.AI.<br>
* [x] Use a simple linear model to make a prediction for the test dataset and evaluate it on Kaggle. (Ensure that the number of rows and the defined IDs match those in the sample\_submission file!)
* [x] Further enrich the dataset with additional variables that may be relevant for estimating revenue and formulate a linear model equation that maximizes the adjusted R² for your training dataset.
* [x] Document the linear regression calculations in the “Baseline Model” directory of your team repository.<br>


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