Creating Your First Predictive Model In Python

Sep 29, 20153 likes8,689 views

If you’ve been reading books and blog posts on machine learning and predictive analytics and are still left wondering how to create a predictive model and apply it to your own data, this presentation will give you the steps you need to take to do just that.

Creating Your First
Predictive Model In Python

Information Everywhere
Makes Panda sad and confused

Each New Thing You Learn
Leads to another new thing to learn, and another…

So Many Things
1. Which predictive modeling technique to use
2. How to get the data into a format for modeling
3. How to ensure the “right” data is being used
4. How to feed the data into the model
5. How to validate the model results
6. How to save the model to use in production
7. How to implement the model in production and apply it to new observations
8. How to save the new predictions
9. How to ensure, over time, that the model is correctly predicting outcomes
10.How to later update the model with new training data

Choose Your Model
http://scikit-learn.org/stable/tutorial/machine_learning_map/

Format The Data
• Pandas FTW!
• Use the map() function to convert any text to a
number
• Fill in any missing values
• Split the data into features (the data) and targets
(the outcome to predict) using .values on the
DataFrame

Get The Right Data
• This is called “Feature selection”
• Univariate feature selection
• SelectKBest removes all but the k highest scoring features
• SelectPercentile removes all but a user-speciﬁed highest scoring
percentage of features using common univariate statistical tests for
each feature: false positive rate
• SelectFpr, false discovery rate SelectFdr, or family wise error SelectFwe.
• GenericUnivariateSelect allows to perform univariate feature selection
with a conﬁgurable strategy.
http://scikit-learn.org/stable/modules/feature_selection.html

Data => Model
1. Build the model
http://scikit-learn.org/stable/modules/cross_validation.html
from sklearn import linear_model
logClassiﬁer = linear_model.LogisticRegression(C=1,
random_state=111)
2. Train the model
from sklearn import cross_validation
X_train, X_test, y_train, y_test = cross_validation.train_test_split(the_data,
the_targets,
cv=12,
test_size=0.20,
random_state=111)
logClassiﬁer.ﬁt(X_train, y_train)

Validation!
1. Accuracy Score
http://scikit-learn.org/stable/modules/cross_validation.html
from sklearn import metrics
metrics.accuracy_score(y_test, predicted)
2. Confusion Matrix
metrics.confusion_matrix(y_test, predicted)

Save the Model
Pickle it!
https://docs.python.org/3/library/pickle.html
import pickle
model_ﬁle = "/lr_classiﬁer_09.29.15.dat"
pickle.dump(logClassiﬁer, open(model_ﬁle, "wb"))
Did it work?
logClassiﬁer2 = pickle.load(open(model, "rb"))
print(logClassiﬁer2)

Implement in Production
• Clean the data the same way you did for the model
• Feature mappings
• Column re-ordering
• Create a function that returns the prediction
• Deserialize the model from the ﬁle you created
• Feed the model the data in the same order
• Call .predict() and get your answer

Save Your Predictions
As you would any other piece of data

Ensure Accuracy Over Time
Employ your minion army, or get more creative

Update the Model
Train it again, but with validated predictions

Robert Dempsey
robertwdempsey
rdempsey
rdempsey
robertwdempsey.com

This document outlines the steps for developing a predictive modeling project in Python: 1) Select an appropriate modeling technique based on the type of problem, amount of data, and other factors. 2) Prepare the data for modeling by formatting, mapping text to numbers, and splitting into features and targets. 3) Validate the model selection by evaluating performance on test data. 4) Implement the trained model in a production environment to make predictions on new data.

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