correct any spelling or grammatical errors in this analysis. Make it seem more professional but maintain the same style of writting ``` In this project, I worked with data that was gathered from 33 different female FitBit users during a one month-long time period (between April-12–2016 and May-12–2016). The data set used here is part of a hypothetical marketing analysis scenario where users provided some fitness tracking information like total distance, calories, activity intensity, heart rate, and date.
The main goal of this project was to find possible trends in smart device usage and to recommend marketing strategies based on the highlights of the final report. 
I started off my analysis by noticing how some users had to be dropped from the sample size due to a sudden halt in their recollection of data, as shown in the image above. This could have been due to a number of different factors such as forgetting to wear the device or an outright reluctance to continue with the recolection of data. 
Another important tactic used to analyze this data was to classify each date into their corresponding day of the week. From here I could see the distribution of the average TotalDistance traveled by users for each individual day of the week.  
Since the TotalDistance and TotalCalories variables are closely related to each other (the more distance you travel the more calories you burn), it was safe to assume that they would also have similar distributions. This was important to understand because it allowed me to start drawing conclusions from the user activity. 
Some of these conclusions were: 
Heart rate and Workout Intensity is also directly related to Distance and Calories Burned
Any conclusions drawn from the analysis will fall into the average range of the twenty-nine users remaining 

By understanding that the users’ average distance traveled was directly related to the intensity of their workout and that in turn to their consumption of calories, I managed to generate a multi-facet chart that showed how users' activity changed hour by hour on each day of the week. (See Fig. 3) 
From this chart I quickly could conclude that the highest intensity activity usually happened around during the later part of the afternoon. That is to say that, most of the calories burned and the most intense heart rhythm occurred during this later part of the day.  
Some of the other conclusions I reached here were that: 
Saturday afternoons were the most active of all
On Saturday nights people tend to sleep later
All days of the week are more or less equally distributed
My analysis concluded with a detailed look at the users’ average heart rate per minute for any given day of the week. And as one would expect, the heart rate had a gradual increase during the day from 6:00 am to 6:00 pm, with its highest rate being in the later afternoon. Meanwhile, in the night hours of the day from 7:00 pm to 5:00 am the heart rate gradually decreased.  
This natural cycle of the human heart rate is known as the circadian rhythm and can be viewed in more detail here.
So, in the end, I was provided with the fitness tracking data of 33 users in which some users decided to not continue with the recollection of data at some point in the experiment. After dropping these probable outliers, I noticed the direct correlation between the average distance, activity, calories burned, and heart rate.  
Therefore, my observations lead me to conclude that some possible recommendations for the marketing department of this hypothetical smart device company could be as followed:
Users are mostly active in the later hours of the afternoon and start to wind down when approaching the evening. Notifications should be sent in the later hours of the day to ensure the availability of the user’s attention. 
The measurement of the distance, heart rate, activity intensity, and calories burned is important to recollect but other factors should also be considered in order to reach more diverse conclusions. 
More test subjects are needed to ensure a more accurate depiction of the users’ behavior 
Understanding the circadian rhythm can further help develop routines and functionalities for the users who are assumed to be healthy active individuals
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