# Three Data Science Projects in Three Days

## A primer to get your portfolio started.

A data science portfolio should contain a variety of projects. It deserves your time and attention to build one that showcases your unique talents and perspectives. There are several ways to code interesting models that can fill out your portfolio. Here are three simple ones to get the ball rolling.

**Day One: Regression**

Fit a simple linear regression model using the `CO2`

dataset in R:

`# Load the CO2 dataset`

data(CO2)

# Fit a linear regression model

model <- lm(conc ~ uptake, data = CO2)

# Print the model summary

summary(model)

In this example, we are fitting a simple linear regression model using the `lm()`

function in R, with `conc`

as the response variable and `uptake`

as the predictor variable from the `CO2`

dataset. The `data`

parameter specifies the dataset to use for the model. The `summary()`

function is then used to print the model summary, which includes information such as coefficients, standard errors, t-statistics, p-values, and goodness-of-fit measures like R-squared.

The `CO2`

dataset contains measurements of CO2 uptake by a plant at different levels of light, temperature, and CO2 concentration. By fitting a linear regression model to this data, we can explore the…