# Machine Learning Capstone Project: OKCupid

Here’s a link to the Github Repo: https://github.com/SpringboLives/CC_SupervisedML_Capstone

Pasting my “Reporting.md” file below. I’m curious if anyone’s had any success in creating a different code for education? I’ve tried two different structures (laid out in the feature_selection.py files) but this is my first attempt and I’d love to hear other ideas. Okay, begin my Reporting file!!

### Project Description

Using a dataset of user information provided by OKCupid and Codecademy, formulate questions about the data that can be explored using machine learning classification and regression algorithms. Generate new columns within the dataset as needed to test your questions.

### Methods and Python modules used

• Python Modules
• Pandas - for viewing and manipulating data (creating new columns, sorting, etc.)
• matplotlib - for plotting and visualizing results
• sklearn - for accessing classification and regression modules
• Classification Algorithms
• Decision Tree
• Random Forest
• K Nearest Neighbor
• Regression Algorithms
• Linear Regression
• Multi Linear Regression

### Data Exploration

• Navigate to Visualizations > explorations.png for my charts
• Age is right skewed
• Sex is made up of 59.77% male and 40.23% female
• Income features an inverted bell, but 48442 (80.81%) users reported income as -1 (this might be OKCupid’s way of showing a NaN)

1. Can body type be predicted with different types of classification algorithms by looking
at diet, drinking, drug use, education, and income level?

2. Is education level an accurate way of predicting income?

I arrived at these questions after exploring the different features that existed in the dataset
and asked questions to myself about what I might be able to glean from the features.

### New Columns Created

• Education
• Created a numeric value to associate with the different levels of education.
This ranks education based on the level (high school < college < masters) and
the level within schooling where the user is (drop out < some school < finished)
• There is a value of “Space Camp” in the education column. I’ve removed this as I
don’t consider it helpful information in trying to create continuous data
for the education column.
• Note: I tried ranking the education values from low to high with no overlapping numbers (0 to 15 with ‘drop out of high school’ being the lowest, and going up by 1 with all graduate-level schools being ranked the same). This version is stored in feature_selection_v2.py if you’d like to see the results there.
``````    work = 'working on '
drop = 'dropped out of '

education_mapping = {
'{}high school'.format(drop): 0,
'{}high school'.format(work): 1,
'high school': 2,
'{}two-year college'.format(drop): 1,
'{}two-year college'.format(work): 2,
'two-year college': 3,
'{}college/university'.format(drop): 2,
'{}college/university'.format(work): 3,
'college/university': 4,
'{}masters program'.format(drop): 3,
'{}masters program'.format(work): 4,
'masters program': 5,
'{}med school'.format(drop): 3,
'{}med school'.format(work): 4,
'med school': 5,
'{}law school'.format(drop): 3,
'{}law school'.format(work): 4,
'law school': 5,
}
``````
• Sign_refined
• This column was meant to change all of the qualifiers following the users’ signs
so they only contained the sign itself.
• The original data contained phrases like “Gemini and laughing about it”, “Gemini but it doesn’t matter”.
``````signs = ['aquarius', 'aries', 'taurus', 'gemini', 'cancer', 'leo',
'virgo', 'libra', 'scorpio', 'sagittarius', 'capricorn', 'pisces'
]

df.dropna(subset=['sign'], inplace=True)
df['sign_refined'] = np.where(df['sign'].str.contains(signs[0]), signs[0],
np.where(df['sign'].str.contains(signs[1]), signs[1],
np.where(df['sign'].str.contains(signs[2]), signs[2],
np.where(df['sign'].str.contains(signs[3]), signs[3],
np.where(df['sign'].str.contains(signs[4]), signs[4],
np.where(df['sign'].str.contains(signs[5]), signs[5],
np.where(df['sign'].str.contains(signs[6]), signs[6],
np.where(df['sign'].str.contains(signs[7]), signs[7],
np.where(df['sign'].str.contains(signs[8]), signs[8],
np.where(df['sign'].str.contains(signs[9]), signs[9],
np.where(df['sign'].str.contains(signs[10]), signs[10],
np.where(df['sign'].str.contains(signs[11]), signs[11],
'No'))))))))))))
``````

### Question 1: Classifier Comparison

• Predicting Body Type from diet, drinking, drug use, education, and income level
• Decision Tree
• Best accuracy = 0.29200652528548127
Best Depth = 5
Time to run (s) = 0.0777902603149414
• Random Forest
• Accuracy = 0.23491027732463296
Time to run = 0.19149017333984375
• K Nearest Neighbor
• Best Accuracy = 0.27569331158238175
Best Neighbor Amt. = 35
Time to run = 2.9052326679229736
• Qualitative Discussion
• The level of simplicity in using each of these three methods was very close,
with Decision Tree being the easiest to implement with the given dataset. I had an
issue getting the shape of the array to be correct in the random forest and k nearest
neighbor algorithms. To get past this, I had to use a `.ravel()` method in the function
definitions I’d created.
• Decision Tree classification showed the highest level of accuracy in predicting
body type from the selected features. The body type ‘average’ is the response in 24%
of the users, so the model is performing better than expected by 4.8%
• Random Forest seems to perform the worst in this scenario.
• Regarding efficiency, Decision tree is the fastest algorithm in this case. I was using
regression to find the best depth and accuracy for Decision Tree and K Nearest Neighbor.
• Decision Tree was only iterating through `range(1, 20)`, while K Nearest Neighbor was iterating through `range(1,100)` when K Nearest neighbor was given a similar range to iterate through, it still took longer than Decision Tree (.45 seconds)

### Question 2: Regression Model Comparison

• Predicting Income based on education level
• Linear Regression
• `model.score` = .1592
• Time to run = 0.002
• Multiple Linear Regression - using education level and essay word count
• `model.score` = .1268
• Time to run = 0.001
• Qualitative Discussion
• The level of simplicity (once I had created the features properly) was about equal for each
of these regression models. The addition of graphing the regression line on the Linear Regression
Model was a nice addition that was also very simple
• The time it took to run each model was negligible, they both run quickly. Though this dataset is not
full due to the amount of rows removed from `df['income_under_100k']`
• Accuracy is not great, and it shows that there is not a very strong
correlation between education and income, though the linear regression line does show an increase
in income as education level increases. When adding in the essay length for the Multi Linear Regression,
the accuracy went down, telling us that essay length doesn’t have a strong correlation to income level
• The `model.coef_` numbers for the two features were 36215.22 and 1955.43, the latter being the `coef_` of essay length

### Overall Conclusions

I’d like to return to my original questions to give my conclusions:

1. Can body type be predicted with different types of classification algorithms by looking
at diet, drinking, drug use, education, and income level?

• The algorithm performed better than expected in this test, but the results are not significant enough to determine that these features are an accurate predictor of body type.
• I’d want to tweak my numbering system for education and diet to see if that increases the accuracy of the model, and I’d want to have data about how much the user exercises and their hobbies.
2. Is education level an accurate way of predicting income?

• The Linear Regression model produced a regression line that showed an increase in income
as level of education increased, but it was not effective at actually predicting income, as the data has many outliers.
• My next steps would be to further clean and inspect the income data to find a representative data set with less outliers. I would like to have individuals’ degree focuses and their grades to determine if income increases with education in certain degree programs OR in students who received good grades.

I took a look at your code and tried using the body type as a label but wondering how you put a categorical datatype into a continuous variable for data processing, or does K-Means do it for you…

I was also running into an error saying that the the KMeans algorithm couldnt process a continuous data series, and when I changed it the model got an accuracy for 99%.

My biggest hangup is knowing how to transform the data frames for preinput as test_features, label in scikitlearn’s train_test_split function…there were still a bunch of NaNs after I dropped frames…

Hmm I need to brush up on K-Means, but it may be that you need to have all continuous data for it to provide labels.

With K-Nearest Neighbor, I was using `body_type` as the categorical label, so I’m assuming that body type isn’t continuous in my code. For it to be used in data processing, you’d have to convert `body_type` into continuous data, which gets tricky because the numeric value would have to have some actual relationship or else it’s just random numbers and doesn’t help you.

I transformed the education data first by sitting with pen and paper thinking about how I would rank the items listed while trying to eliminate bias from the data. You could do this with body type, I assume, but I think body type is independent where education level can be seen as continuous. Quoting my original post for reference:

Education

• Created a numeric value to associate with the different levels of education.
This ranks education based on the level (high school < college < masters) and
the level within schooling where the user is (drop out < some school < finished)
• There is a value of “Space Camp” in the education column. I’ve removed this as I
don’t consider it helpful information in trying to create continuous data
for the education column.
• Note: I tried ranking the education values from low to high with no overlapping numbers (0 to 15 with ‘drop out of high school’ being the lowest, and going up by 1 with all graduate-level schools being ranked the same). This version is stored in feature_selection_v2.py if you’d like to see the results there.

Hope that helps!

selected_labels = [‘body_type’]
for selected_label in selected_labels:
feature_data = df[selected_features + [selected_label]].dropna(axis=0)

``````labels = np.array(feature_data[selected_label])
features = feature_data[selected_features]

x = features.values
scaler = preprocessing.MinMaxScaler()
features = scaler.fit_transform(x)

features_train, features_test, labels_train, labels_test = train_test_split(features, labels, test_size=0.2, random_state=23)
``````

Can you explain the for loop as mentioned above, why are you iterating through a single column
when it can simply be chosen as a feature? Is it to change the label to another category such as ‘education_level’, ‘smokes’, or ‘income’?

Can you use K-means for this dataset? I see you used K-NN which is another a supervised learning algorithm, so would there be a difference in how you preprocess and train the model?

You can put multiple column names in the list `selected_labels` and it will spit out the results of the tests for each label. It was a way for me to loop through different columns of labels and see which ones were worth digging further into. So this was an exploratory bit of code that I ended up doing away with. Thanks for that question, though. I will clarify that in my comments