# Data Science Project on President Heights

If you are a beginner in Data Science you must solve this project, as you will learn a lot about working on Data, that comes from a csv file or any other formats.

This data is available in the file heights.csv, which is a simple comma-separated list of labels and values:

`print(data.head())`

We’ll use the Pandas package to read the file and extract this information (note that the heights are measured in centimeters):

`height = np.array(data["height(cm)"])`

`print(height)`

Now that we have this data array, we can compute a variety of summary statistics:

`print("Standard Deviation of height =", height.std())`

`print("Minimum height =", height.min())`

```
print("Maximum height =", height.max())
```

Note that in each case, the aggregation operation reduced the entire array to a single summarizing value, which gives us information about the distribution of values. We may also wish to compute quantiles:

`print("Median =", np.median(height))`

```
print("75th percentile =", np.percentile(height, 75))
```

We see that the median height of US presidents is 182 cm, or just shy of six feet. Of course, sometimes it’s more useful to see a visual representation of this data, which we can accomplish using tools in Matplotlib:

`import matplotlib.pyplot as plt`

`import seaborn as sns`

`sns.set()`

`plt.title("Height Distribution of Presidents of USA")`

`plt.xlabel("height(cm)")`

`plt.ylabel("Number")`

`plt.show()`

These aggregates are some of the fundamental pieces of exploratory data science that we’ll explore in more depth in later coming projects.

# Python Example for Beginners

## Two Machine Learning Fields

There are two sides to machine learning:

**Practical Machine Learning:**This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.**Theoretical Machine Learning**: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

**Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes**

**Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!**

Latest end-to-end Learn by Coding Recipes in Project-Based Learning:

**Applied Statistics with R for Beginners and Business Professionals**

**Data Science and Machine Learning Projects in Python: Tabular Data Analytics**

**Data Science and Machine Learning Projects in R: Tabular Data Analytics**

**Python Machine Learning & Data Science Recipes: Learn by Coding**

**R Machine Learning & Data Science Recipes: Learn by Coding**

**Comparing Different Machine Learning Algorithms in Python for Classification (FREE)**

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause.The information presented here could also be found in public knowledge domains.