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This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -1,3 +1,3 @@ # Data Science Workshop Moved to <https://github.com/asimjalis/data-science-workshop> -
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This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -1,14 +0,0 @@ This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -1,208 +0,0 @@ This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -1,435 +0,0 @@ This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,3 @@ # Data Science Workshop See <https://github.com/asimjalis/data-science-workshop> This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -1,892 +0,0 @@ -
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This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -4,7 +4,8 @@ This lab will be a lot easier if you use the Titanic demo as a guide. This lab will be a lot easier if you use the [Titanic demo](https://gist.github.com/asimjalis/c2412812b8e2d6039e3f5c48e8134e25#file-titanic-ipynb ) as a guide. ## Overview -
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This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -1,10 +1,10 @@ # Data Science Workshop Demo and Lab This gist is for the *Data Science and Machine Learning Using Python scikit-learn* workshop. The workshop is part of DataEngConf 2016, held on April 7-8 2016, at Galvanize, 44 Tehama Street, San Francisco, CA. The gist contains the following demo, lab, and data files. Use the direct links to easily navigate to each file. File |Contents |Link ---- |-------- |---- -
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This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -6,9 +6,9 @@ The workshop is a part of the DataEngConf 2016, on April 7-8 2016, held at Galva The gist contains the following demo, lab, and data files. File |Contents |Link ---- |-------- |---- `titanic.ipynb` |Titanic Data Analysis IPython Notebook |[link](https://gist.github.com/asimjalis/c2412812b8e2d6039e3f5c48e8134e25#file-titanic-ipynb) `titanic.csv` |Titanic Dataset |[link](https://gist.github.com/asimjalis/c2412812b8e2d6039e3f5c48e8134e25#file-titanic-csv) `congress-lab.md` |Congress Voting Lab |[link](https://gist.github.com/asimjalis/c2412812b8e2d6039e3f5c48e8134e25#file-congress-lab-md) `congress.csv` |Congress Voting Dataset |[link](https://gist.github.com/asimjalis/c2412812b8e2d6039e3f5c48e8134e25#file-congress-csv) -
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This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,207 @@ # Congress Voting Data ## Note This lab will be a lot easier if you use the Titanic demo as a guide. You can view the Titanic demo at <https://gist.github.com/asimjalis/722c72622a672ad9e47cd5b2f695f79a>. ## Overview The data for this lab is in the attached file called `congress.csv`. Each line corresponds to a congressperson, which party they belong to, and how they voted on 16 different issues. The dataset is from UCI [here](https://archive.ics.uci.edu/ml/datasets/Congressional+Voting+Records). Here are the meanings of the columns in the CSV file. Column |Values ------ |------ `party` |`democrat` ` republican` `handicapped-infants` |`?` `n` `y` `water-project-cost-sharing` |`?` `n` `y` `adoption-of-the-budget-resolution` |`?` `n` `y` `physician-fee-freeze` |`?` `n` `y` `el-salvador-aid` |`?` `n` `y` `religious-groups-in-schools` |`?` `n` `y` `anti-satellite-test-ban` |`?` `n` `y` `aid-to-nicaraguan-contras` |`?` `n` `y` `mx-missile` |`?` `n` `y` `immigration` |`?` `n` `y` `synfuels-corporation-cutback` |`?` `n` `y` `education-spending` |`?` `n` `y` `superfund-right-to-sue` |`?` `n` `y` `crime` |`?` `n` `y` `duty-free-exports` |`?` `n` `y` `export-administration-act-south-africa` |`?` `n` `y` Based on the votes on the 16 issues we are going to predict the party using Random Forests. # Lab 1: Data Analysis ## Download Data The data is in `congress.csv` which is attached to this gist. Look at the CSV data. Looks like it does not have any headers. ## Load Data Import packages. import pandas as pd import numpy as np Load the CSV. Since the CSV file does not have headers so we load it with `header=None`. df = pd.read_csv('data/congress.csv', header=None) Do a quick sanity check to see that it loaded. df.head() ## Naming Columns Lets give the columns names. First define some column names. df_columns = [ "party", "infants", "water", "budget", "physician", "el_salvador", "religious", "satellite", "contras", "missile", "immigration", "synfuels", "education", "superfund", "crime", "exports", "south_africa", ] Now assign these to `df.columns`. df.columns = df_columns Check that the columns now have these names. df.head() ## Binarizing Data Random Forests and other machine learning algorithms expect the data to be in a numeric format. We have to replace all the strings with numbers. In our case the values are mostly binary so we will replace them with 0 and 1. Modify the DataFrame to replace `democrat` with 0 and `republican` with 1. Then save this result to a DataFrame. Next, modify the DataFrame to replace `n` and `?` with 0 and `y` with 1 in all 16 columns. Save this result to a DataFrame. ## Abstain Columns Is it reasonable to convert `?`, `n`, `y` to 2 values? Should we have converted it to 3? For each column we could have added a new column which was 0 or 1 if the congressperson abstained. This would give us another 16 features. The upside is that our model might perform better. The downside is that the model might take longer to train with all the additional dimensions (due to the *curse of dimensionality*). Instead we are making an assumption here that abstaining is a weaker form of `n` and so we are grouping them together. ## Analyzing Data Before we build a model we want to analyze the data to figure out if there are patterns that stand out. This will give us a good intuition about the data and suggest how to approach it. ## Water Project Cost Sharing Lets calculate what percentage of Democrats and Republicans support the bill called `water-project-cost-sharing`. To do this group the records by party, and then calculate the mean value of the `water-project-cost-sharing` field. Create a scatter matrix of the data. ## Analyzing Voting Across Issues Extra Credit: Using the same technique as in the previous exercise calculate the percentages of Democrats and Republicans who supported each of the 16 bills. # Lab 2: Machine Learning Workflow ## Preparation: Defining X and y Next we are going prepare the data for feeding into our machine learning algorithm. scikit-learn uses Numpy arrays. We are going to define `X` and `y` from our DataFrame and save them as Numpy arrays. Put the output, the party affiliation, column index 0, into `y`. To do this slice the DataFrame so that you just get the first column. Then apply `.values` to this to turn it into a Numpy array. Put the inputs, the 16 voting columns, column indexes 1-16, into a `X`. To do this slice the DataFrame so you get the second through the last column values. Then apply `.values` to this to turn it into a Numpy matrix. ## Preparation: Split 70/30 Do a 70%-30% split of the the DataFrame into a training set and a test set. Use the scikit-learn method `train_test_split` to do this. Save the training set as `X_train` and `y_train`. Save the test set as `X_test` and `y_test`. ## Training: Random Forest Model Next we will predict the party affiliation of a congressperson based on their voting record. Fit a Random Forest model with `n_estimators=100`. ## Evaluation: Using Test Data Evaluate this model on the test set. How well did it do? ## Evaluation: Using Training Data Evaluate this model on the training set. How well did it do? Did it do better or worse than on the training data? Are you concerned about over-fitting? ## Cross Validation Use 10-fold cross validation to check if the performance of your model holds. How well does your model do? ## Optimization: Tuning Hyperparameters Next tune the hyperparameters using Grid Search. Here are the choices that make up our grid. Hyperparameters |Values --------------- |------ `n_estimators` |10,20,50,100,200,500 `max_depth` |1,3,5,10,20 `max_features` |1,2,4,8,16 Did tuning hyperparameters make the model improve measurably? Would you consider the optimization step to be worthwhile? This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,435 @@ republican,n,y,n,y,y,y,n,n,n,y,?,y,y,y,n,y republican,n,y,n,y,y,y,n,n,n,n,n,y,y,y,n,? democrat,?,y,y,?,y,y,n,n,n,n,y,n,y,y,n,n democrat,n,y,y,n,?,y,n,n,n,n,y,n,y,n,n,y democrat,y,y,y,n,y,y,n,n,n,n,y,?,y,y,y,y democrat,n,y,y,n,y,y,n,n,n,n,n,n,y,y,y,y democrat,n,y,n,y,y,y,n,n,n,n,n,n,?,y,y,y republican,n,y,n,y,y,y,n,n,n,n,n,n,y,y,?,y republican,n,y,n,y,y,y,n,n,n,n,n,y,y,y,n,y democrat,y,y,y,n,n,n,y,y,y,n,n,n,n,n,?,? republican,n,y,n,y,y,n,n,n,n,n,?,?,y,y,n,n republican,n,y,n,y,y,y,n,n,n,n,y,?,y,y,?,? democrat,n,y,y,n,n,n,y,y,y,n,n,n,y,n,?,? democrat,y,y,y,n,n,y,y,y,?,y,y,?,n,n,y,? republican,n,y,n,y,y,y,n,n,n,n,n,y,?,?,n,? republican,n,y,n,y,y,y,n,n,n,y,n,y,y,?,n,? democrat,y,n,y,n,n,y,n,y,?,y,y,y,?,n,n,y democrat,y,?,y,n,n,n,y,y,y,n,n,n,y,n,y,y republican,n,y,n,y,y,y,n,n,n,n,n,?,y,y,n,n democrat,y,y,y,n,n,n,y,y,y,n,y,n,n,n,y,y democrat,y,y,y,n,n,?,y,y,n,n,y,n,n,n,y,y democrat,y,y,y,n,n,n,y,y,y,n,n,n,?,?,y,y democrat,y,?,y,n,n,n,y,y,y,n,n,?,n,n,y,y democrat,y,y,y,n,n,n,y,y,y,n,n,n,n,n,y,y democrat,y,n,y,n,n,n,y,y,y,n,n,n,n,n,y,? 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republican,y,y,n,y,y,y,n,n,n,y,n,y,y,y,n,n democrat,y,y,y,n,n,y,?,y,n,n,y,y,n,y,n,? republican,n,y,n,y,y,y,n,n,n,y,y,y,y,y,n,n republican,n,y,n,y,y,y,n,n,n,y,y,y,y,y,n,y republican,n,y,n,y,y,y,n,n,n,y,n,y,y,y,n,y republican,n,y,n,y,y,y,n,n,n,y,n,y,y,y,n,y republican,n,y,n,y,y,y,n,n,n,y,n,y,y,y,n,? democrat,y,y,y,n,n,?,y,y,y,y,n,n,n,n,y,? republican,n,y,n,y,y,y,n,n,n,n,n,y,y,y,n,n democrat,y,y,y,n,n,n,y,y,y,n,n,n,n,n,n,? democrat,y,y,y,n,n,n,y,y,y,n,y,n,n,n,n,y democrat,y,y,y,n,n,n,y,y,y,n,y,?,n,n,n,y republican,y,y,n,y,y,y,y,n,n,n,n,y,y,y,n,y republican,n,y,n,y,y,y,y,n,n,n,y,y,y,y,n,y republican,n,y,n,y,y,y,n,n,n,y,n,y,y,y,n,n democrat,y,?,y,n,n,n,y,y,y,n,n,n,y,n,y,y democrat,y,y,y,n,n,n,y,y,y,n,n,n,n,n,y,y democrat,y,n,y,n,n,n,y,y,y,n,n,n,y,n,y,? republican,y,y,y,y,n,n,y,y,y,y,y,n,n,y,n,y democrat,y,y,y,n,n,n,y,y,y,n,y,n,n,n,y,? republican,y,n,y,y,y,n,y,n,y,y,n,n,y,y,n,y democrat,y,n,y,n,n,y,y,y,y,y,y,n,n,y,y,y democrat,n,y,y,y,y,y,n,n,n,y,y,n,y,y,n,n democrat,n,y,y,n,y,y,n,n,n,y,y,y,y,y,n,? democrat,n,y,y,y,y,y,n,y,y,y,y,y,y,y,n,y democrat,y,y,y,n,y,y,n,n,n,y,y,n,y,y,n,y republican,n,n,n,y,y,n,n,n,n,y,n,y,y,y,n,n democrat,y,n,y,n,n,y,y,y,y,y,n,y,n,y,n,? democrat,y,n,y,n,n,n,y,y,?,y,y,y,n,y,n,y republican,n,n,n,y,y,y,n,n,n,y,n,y,y,y,n,y republican,n,n,n,y,y,y,n,n,n,n,n,y,y,y,n,n republican,n,?,n,y,y,y,n,n,n,y,n,y,y,y,n,n democrat,n,n,y,n,y,y,n,n,n,y,y,y,y,y,n,y republican,n,n,n,y,y,y,n,n,n,y,n,y,y,y,n,n republican,n,n,n,y,y,y,n,n,n,n,n,y,y,y,n,n democrat,n,y,y,n,y,y,y,n,y,y,y,n,y,y,n,y republican,n,n,n,y,y,y,n,n,n,y,n,?,y,y,n,? democrat,y,n,y,n,n,n,y,y,y,y,n,n,n,n,y,y democrat,y,n,y,n,n,n,y,y,y,y,y,n,n,n,y,y democrat,y,y,y,n,n,n,y,y,n,y,y,n,n,?,y,y democrat,y,n,y,n,n,n,y,n,y,y,y,n,n,n,y,y democrat,y,n,y,n,y,y,n,n,n,n,n,n,n,n,n,y democrat,y,n,y,n,y,y,n,?,?,n,y,?,?,?,y,y democrat,n,n,?,n,y,y,n,n,n,n,y,y,y,y,n,y democrat,y,n,n,n,y,y,y,n,n,y,y,n,n,y,n,y democrat,y,y,y,n,n,y,y,y,y,y,n,n,n,n,n,y republican,n,n,n,y,y,y,n,n,n,y,?,y,y,y,n,n democrat,y,n,n,n,y,y,n,n,n,n,y,y,n,y,n,y democrat,y,n,y,n,y,y,y,n,n,n,y,n,n,y,n,y democrat,y,n,y,n,y,y,y,n,?,n,y,n,y,y,y,? democrat,y,n,n,n,y,y,?,n,?,n,n,n,n,y,?,n democrat,?,?,?,?,n,y,y,y,y,y,?,n,y,y,n,? democrat,y,y,y,n,n,n,n,y,y,n,y,n,n,n,y,y republican,n,y,n,y,y,y,n,n,n,n,n,y,y,y,n,y republican,n,?,?,?,?,?,?,?,?,?,?,?,?,y,?,? democrat,y,?,y,n,n,n,y,y,y,n,n,n,n,n,y,? democrat,y,?,y,n,n,n,y,y,y,n,n,n,n,n,y,? democrat,n,n,y,n,n,n,y,y,y,y,n,n,n,n,y,y republican,n,?,n,y,y,y,n,n,n,y,n,y,y,y,n,y democrat,n,?,y,n,n,y,y,y,n,y,n,n,n,n,y,? republican,n,?,n,y,y,y,n,n,n,y,n,y,y,y,n,n democrat,y,?,y,n,n,n,y,y,y,n,n,n,n,n,y,? democrat,n,?,y,n,?,?,y,y,y,y,?,?,n,n,y,y democrat,y,n,y,n,n,n,y,y,y,n,y,n,n,n,y,y republican,y,y,y,y,y,n,y,n,n,n,n,y,y,y,n,y democrat,n,y,y,n,n,n,n,y,y,y,y,n,n,n,y,y republican,n,n,n,y,y,y,n,n,n,n,n,y,y,y,n,n republican,n,?,?,y,y,y,n,n,n,y,n,y,y,y,?,y republican,n,?,n,y,y,y,n,n,n,y,n,y,y,y,n,y republican,n,n,n,y,y,y,n,n,n,y,n,y,n,y,n,y republican,y,?,n,y,y,y,n,y,n,n,n,y,y,y,n,y democrat,n,?,y,n,n,n,y,y,y,n,n,n,n,n,y,y republican,n,?,n,y,y,y,n,n,n,y,n,y,y,y,n,y republican,n,?,n,y,y,y,n,n,n,n,n,y,y,y,n,n democrat,n,?,y,n,n,n,y,y,y,y,y,n,n,y,y,y democrat,n,?,y,n,n,y,n,y,n,y,y,n,n,n,y,y democrat,?,?,y,n,n,n,y,y,?,n,?,?,?,?,?,? democrat,y,?,y,n,?,?,y,y,y,n,n,n,n,n,y,? democrat,n,n,y,n,n,y,n,y,y,y,n,n,n,y,n,y republican,n,n,n,y,y,y,n,n,n,y,n,y,y,y,n,? republican,n,n,n,y,y,y,n,n,n,y,n,y,y,y,n,y republican,n,n,n,y,y,y,n,n,n,n,n,y,y,y,n,? republican,n,n,n,y,y,y,n,n,n,y,n,y,y,y,n,n republican,n,y,n,y,y,y,n,n,n,y,y,y,y,n,n,y democrat,n,?,y,n,n,y,y,y,y,y,n,n,n,y,y,y democrat,n,n,y,n,n,y,y,y,y,y,n,n,n,y,n,y democrat,y,n,y,n,n,y,y,y,y,n,n,n,n,n,y,y republican,n,n,n,y,n,n,y,y,y,y,n,n,y,y,n,y republican,n,n,n,y,y,y,y,y,y,y,n,y,y,y,?,y republican,n,n,n,y,y,y,y,y,y,y,n,y,y,y,n,y democrat,?,y,n,n,n,n,y,y,y,y,y,n,n,y,y,y democrat,n,?,n,n,n,y,y,y,y,y,n,n,n,y,n,? democrat,n,n,y,n,n,y,y,y,y,y,n,n,n,y,?,y republican,n,y,n,y,y,y,n,n,n,n,n,y,y,y,n,y democrat,n,n,n,n,n,n,y,y,y,y,n,y,y,y,y,y republican,n,y,n,y,y,y,n,n,n,y,y,y,y,y,n,y democrat,n,n,y,n,n,n,y,y,y,y,n,n,y,n,y,y republican,y,y,n,y,y,y,n,n,n,y,n,y,y,y,n,y democrat,y,y,?,y,y,y,n,n,y,n,y,?,y,y,n,n democrat,n,y,y,n,n,y,n,y,y,y,y,n,y,n,y,y democrat,n,n,y,n,n,y,y,y,y,y,y,n,y,y,n,y republican,n,y,n,y,y,y,n,n,n,n,n,y,y,y,n,n republican,y,y,n,y,y,y,n,?,n,n,y,y,y,y,n,n republican,y,y,n,y,y,y,y,n,n,n,n,y,y,y,n,n democrat,n,y,y,n,n,y,n,y,y,n,y,n,?,?,?,? republican,n,y,n,y,y,y,n,n,n,y,n,y,y,y,n,n democrat,n,y,y,n,?,y,y,y,y,y,y,n,n,?,n,? democrat,n,y,n,n,y,y,n,n,n,n,n,y,y,y,y,y democrat,n,n,n,n,y,y,y,n,n,n,n,y,y,y,n,y democrat,n,y,y,n,y,y,y,n,n,n,y,y,y,y,n,y republican,n,y,n,y,y,y,y,n,n,n,n,y,y,y,n,y democrat,y,y,n,n,y,y,n,n,n,y,y,y,y,y,n,? democrat,n,y,y,n,n,y,y,y,y,y,y,n,y,n,y,? republican,y,n,y,y,y,y,y,y,n,y,n,y,n,y,y,y republican,y,n,y,y,y,y,y,y,n,y,y,y,n,y,y,y democrat,n,n,y,y,y,y,n,n,y,n,n,n,y,y,y,? democrat,y,n,y,n,n,n,y,y,y,y,y,n,n,y,n,y democrat,y,n,y,n,n,n,?,y,y,?,n,n,n,n,y,? republican,n,?,n,y,y,y,n,n,n,y,n,y,y,y,n,y democrat,n,y,y,n,n,n,y,y,y,y,n,n,?,n,y,y democrat,n,n,n,n,y,y,n,n,n,y,y,y,y,y,n,y democrat,y,?,y,n,n,n,y,y,y,n,n,n,n,n,y,? democrat,n,y,y,n,n,n,y,y,y,y,n,n,n,n,y,y republican,n,n,y,y,n,n,y,y,y,y,n,n,n,y,y,y democrat,n,n,y,n,n,n,y,y,y,y,y,?,n,n,y,y democrat,?,n,y,n,n,n,y,y,y,y,y,?,n,n,y,? democrat,y,n,y,n,n,n,y,y,y,y,n,n,n,n,y,y democrat,?,?,y,n,n,n,y,y,y,?,?,n,n,n,?,? democrat,n,n,y,n,n,n,y,y,y,y,y,n,n,n,y,y democrat,y,?,y,n,n,n,y,y,y,n,n,n,n,n,y,y democrat,?,?,?,?,?,?,?,?,y,?,?,?,?,?,?,? democrat,n,n,y,n,n,n,y,y,y,y,y,n,n,n,y,y democrat,y,n,y,n,n,n,y,y,y,y,n,?,n,n,y,y democrat,n,y,y,n,n,n,y,y,y,y,y,n,n,n,y,y democrat,y,n,y,n,n,n,y,y,y,n,n,n,n,n,y,? republican,y,?,n,y,y,y,y,y,n,n,n,y,?,y,?,? democrat,y,n,y,n,n,n,y,y,y,n,n,n,n,n,y,y republican,n,?,n,y,y,y,n,n,n,n,n,y,y,y,n,? republican,n,y,n,y,y,y,n,?,n,y,n,y,y,y,n,? democrat,n,n,n,n,n,y,y,y,y,n,y,n,n,y,y,y democrat,n,n,y,n,n,n,y,y,y,n,n,n,n,n,y,y democrat,n,n,y,n,n,y,y,?,y,y,y,n,n,n,y,y republican,n,n,n,y,y,y,n,n,n,n,n,y,y,y,n,? democrat,n,n,y,n,n,y,y,y,y,n,y,y,n,y,y,? republican,n,?,y,y,y,y,n,n,n,y,n,n,n,y,n,y democrat,n,n,y,n,n,n,y,y,y,y,y,n,?,n,y,? democrat,y,y,n,n,n,n,y,y,?,n,y,n,n,n,y,? democrat,n,n,y,n,n,n,y,y,y,n,n,n,n,y,y,y democrat,y,y,y,n,n,n,y,y,y,n,n,n,n,n,y,y democrat,y,n,y,n,n,y,y,y,y,y,y,n,n,n,y,y democrat,y,n,y,n,n,n,y,y,y,y,n,n,n,n,y,y republican,n,n,y,y,y,y,y,n,n,n,n,y,y,y,n,y democrat,n,n,y,n,n,y,y,y,y,y,n,y,n,n,n,y republican,n,n,n,y,y,y,n,n,n,y,n,y,n,y,n,y republican,y,?,n,y,y,y,y,n,n,y,n,y,y,y,n,y democrat,n,n,y,n,n,n,y,y,y,n,n,?,n,n,y,y democrat,y,y,y,n,n,n,y,y,y,y,y,n,n,n,n,y democrat,n,n,y,n,n,y,y,y,y,n,n,n,n,n,y,y republican,n,y,n,y,y,y,n,n,n,y,n,y,y,y,n,y democrat,n,n,y,n,n,n,y,y,y,n,y,n,n,n,y,y democrat,n,y,y,n,n,y,n,y,y,n,y,n,y,n,y,y republican,y,y,n,y,y,y,n,n,n,y,n,y,y,y,n,y democrat,n,y,y,y,y,y,n,n,n,y,y,y,y,y,y,? democrat,y,y,y,n,y,y,n,n,?,y,n,n,n,y,y,? republican,n,y,n,y,y,y,n,n,n,y,n,y,y,y,n,n democrat,y,?,y,n,n,n,y,y,y,n,?,n,n,n,y,? democrat,n,y,y,n,n,n,n,y,y,n,y,n,n,y,y,y democrat,n,n,y,n,n,n,y,y,y,n,n,n,n,n,y,? democrat,n,y,y,n,y,y,n,n,n,n,y,n,n,n,y,? democrat,y,n,y,n,n,n,y,y,y,n,y,n,n,n,y,? republican,n,n,n,y,y,n,n,n,n,n,n,y,y,y,n,y republican,n,y,n,y,y,y,n,n,n,y,n,?,y,y,n,n republican,n,?,n,y,y,y,n,n,n,n,n,y,y,y,n,y democrat,n,n,y,n,n,y,y,y,y,n,y,n,n,y,y,y democrat,y,n,y,n,n,n,y,y,y,n,n,n,n,n,?,y republican,n,y,n,y,y,y,n,n,n,n,n,y,y,?,n,y republican,n,y,y,y,y,y,y,n,y,y,n,y,y,y,n,y republican,n,y,n,y,y,y,n,n,n,n,n,y,y,y,n,y republican,n,y,n,y,y,y,n,n,y,y,n,y,y,y,n,y democrat,n,y,y,n,n,n,y,y,n,n,y,n,n,n,y,? republican,n,y,n,y,y,y,n,n,n,y,n,y,y,y,n,y democrat,n,n,y,n,n,y,y,y,y,y,n,y,n,y,y,? republican,n,n,n,y,y,y,n,n,n,y,n,y,n,y,n,y democrat,n,n,y,n,n,n,y,y,y,n,n,n,n,n,y,y democrat,y,n,y,n,n,y,y,y,n,n,n,y,y,n,n,y democrat,y,y,y,n,n,n,y,y,?,y,n,n,n,n,y,? republican,n,n,n,y,y,y,y,n,n,y,n,n,n,y,y,y republican,n,n,n,y,n,y,y,?,y,n,n,y,y,y,n,y democrat,y,n,y,n,n,n,y,y,y,y,y,n,n,y,y,y republican,n,n,n,n,y,y,y,n,n,n,n,?,n,y,y,y democrat,n,y,y,n,n,n,y,y,?,y,n,n,y,n,y,y democrat,y,n,y,n,n,n,n,y,y,y,n,n,n,n,y,y democrat,y,n,y,n,n,n,y,y,y,y,y,n,n,n,y,y democrat,n,n,y,n,y,n,y,y,y,n,n,n,n,y,?,y republican,n,y,n,y,y,y,?,n,n,n,n,?,y,y,n,n republican,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,? democrat,y,n,y,n,n,n,y,y,?,n,y,n,n,n,y,y republican,n,y,n,y,y,y,n,n,n,n,n,y,y,y,n,n republican,n,y,n,y,y,y,n,n,n,n,n,y,y,y,n,n democrat,y,y,y,n,n,y,y,y,y,n,n,n,n,n,y,y republican,n,y,n,y,y,y,n,n,n,n,n,y,y,y,n,y democrat,y,n,y,n,n,n,y,y,y,y,n,n,n,n,n,y democrat,y,n,y,n,n,n,y,y,y,y,n,n,n,y,y,y republican,n,n,n,y,y,n,n,n,n,n,n,y,n,y,n,n republican,n,n,n,y,y,n,n,n,n,n,n,y,n,y,?,y democrat,n,n,y,n,n,n,y,y,y,n,y,n,n,n,y,y democrat,y,n,y,n,n,n,y,y,y,n,n,n,n,n,n,y democrat,y,n,y,n,n,n,y,y,y,y,n,n,n,n,n,y democrat,y,n,y,n,n,?,y,y,y,n,?,?,n,?,?,? democrat,y,n,y,n,n,n,y,y,y,y,n,n,?,n,y,y democrat,y,n,y,n,n,n,y,y,y,n,n,n,n,n,y,? democrat,y,n,y,n,n,n,y,y,y,n,n,n,n,n,y,? democrat,y,n,y,n,n,n,y,y,y,y,n,n,n,n,n,y republican,n,n,n,y,y,y,n,n,n,y,n,y,n,y,n,y republican,y,n,n,n,n,n,y,y,y,y,n,n,n,y,n,y democrat,y,n,y,n,n,n,y,y,y,n,n,n,n,n,y,? democrat,y,n,y,n,n,n,y,y,y,n,n,n,n,n,n,y democrat,y,y,y,n,n,n,y,y,y,n,n,n,n,n,y,y democrat,n,y,y,n,n,y,y,y,y,n,?,n,n,n,n,y democrat,y,n,y,n,n,n,y,y,y,y,n,n,n,n,y,? republican,n,n,n,y,y,n,y,y,n,y,n,y,y,y,?,y republican,y,n,n,y,y,n,y,n,n,y,n,n,n,y,y,y democrat,n,n,y,n,y,y,n,n,n,n,?,n,y,y,n,n republican,n,n,n,y,y,y,n,n,n,n,n,y,y,y,y,n republican,n,n,y,y,y,y,y,y,n,y,n,n,n,y,n,y republican,n,n,n,y,y,y,n,n,n,n,n,y,y,y,n,y republican,n,n,n,y,y,y,n,n,n,y,n,y,y,y,n,n democrat,n,n,y,n,n,n,y,y,y,y,n,n,n,y,n,y republican,y,n,y,y,y,y,y,y,n,n,n,n,n,y,n,? republican,y,n,n,y,y,y,n,n,n,y,n,?,y,y,n,n republican,n,n,n,y,y,y,n,n,n,n,n,y,y,y,n,y democrat,n,n,y,n,n,y,y,y,y,y,y,n,n,n,?,y democrat,n,n,y,n,n,y,y,y,y,y,y,n,n,n,y,y democrat,n,n,y,n,n,y,?,y,?,y,y,y,n,y,y,? democrat,y,y,y,?,n,y,y,y,y,n,y,n,y,n,?,y democrat,y,y,y,n,y,y,n,y,n,y,y,n,y,y,y,y democrat,y,y,y,n,y,y,n,y,n,y,y,n,y,y,n,? democrat,y,n,y,n,?,y,?,y,y,y,n,n,y,y,n,y democrat,y,n,y,n,n,y,y,y,y,y,n,?,n,y,n,y democrat,y,n,y,n,n,y,y,y,n,y,y,n,y,y,y,y democrat,y,y,y,n,n,y,y,y,y,y,y,n,y,y,y,y democrat,n,y,y,n,n,y,y,y,n,y,y,n,y,y,n,? republican,n,y,n,y,y,y,?,?,n,y,n,y,?,?,?,? republican,n,n,y,y,y,y,n,n,n,y,n,y,y,y,y,y democrat,y,y,y,n,n,y,y,y,y,y,n,n,?,n,y,? democrat,n,y,n,n,n,n,y,y,y,y,y,n,n,n,y,y democrat,n,y,y,n,n,y,y,y,y,y,n,n,y,y,y,y republican,n,n,n,y,y,n,y,y,y,y,n,y,y,y,n,y democrat,n,n,?,n,n,y,y,y,y,n,n,n,n,n,y,y republican,n,n,n,y,y,y,y,n,n,y,n,y,y,y,n,y republican,n,n,n,y,y,y,n,n,n,n,n,y,y,y,n,n republican,n,y,n,y,y,y,n,n,n,y,n,y,y,y,n,? republican,n,n,n,y,y,y,n,n,n,y,n,y,y,y,n,n republican,n,n,n,y,y,y,n,n,n,n,n,y,y,y,n,n democrat,y,n,y,n,n,y,y,y,y,n,n,n,n,y,n,? republican,n,n,n,y,y,y,n,n,n,y,n,y,y,y,n,n democrat,y,n,n,n,n,y,y,y,y,y,n,n,n,y,y,y republican,n,n,n,y,y,y,n,n,n,y,n,y,y,y,y,n democrat,n,n,y,n,n,y,y,y,y,y,n,n,y,n,n,y democrat,y,y,y,n,n,n,y,y,y,y,n,n,n,n,y,y republican,n,y,y,y,y,y,n,n,n,y,n,y,y,y,n,y republican,n,y,n,y,y,y,y,y,n,n,y,y,y,y,y,y republican,n,y,y,y,y,y,y,?,n,n,n,n,?,?,y,? democrat,n,n,n,n,n,y,n,y,y,n,y,y,y,y,y,n democrat,y,n,n,n,n,n,y,y,y,y,n,n,n,n,y,y democrat,n,n,y,n,n,n,y,y,y,n,n,n,n,n,y,? democrat,y,n,y,n,n,n,y,y,y,n,n,n,n,n,y,? democrat,n,y,y,n,n,y,n,y,y,y,n,n,y,y,n,y democrat,y,y,y,n,n,n,y,y,y,y,n,n,y,n,n,y democrat,y,y,y,n,?,y,n,?,n,n,y,n,y,y,n,? democrat,y,y,y,n,y,y,n,y,?,y,n,n,y,y,n,? republican,n,y,n,y,y,y,n,n,n,n,y,y,y,y,n,n democrat,n,y,n,n,y,y,n,n,?,n,n,y,y,y,n,y democrat,y,y,n,y,n,n,y,y,y,n,y,n,n,y,n,y republican,n,y,n,y,y,y,n,n,n,n,n,y,y,y,n,y democrat,y,y,y,n,n,n,y,y,y,n,y,n,n,n,n,y democrat,y,?,y,n,n,y,y,y,y,y,n,n,n,n,y,? republican,n,y,n,y,y,y,n,n,n,y,n,y,y,y,n,n democrat,y,?,y,n,n,n,y,y,y,n,n,n,n,n,y,? democrat,y,n,y,n,n,n,y,y,y,n,y,n,n,n,y,? democrat,n,n,y,n,n,n,y,y,y,n,n,n,n,n,y,y democrat,n,y,y,n,n,y,y,y,?,n,y,y,n,n,y,y republican,n,n,n,y,y,y,n,n,n,y,y,y,y,y,n,? democrat,n,n,y,n,n,y,y,y,n,n,y,n,n,y,?,y democrat,y,n,y,n,n,n,y,y,y,n,n,n,n,n,y,y democrat,y,n,y,n,n,n,y,y,y,y,n,n,n,y,y,y republican,y,n,n,y,y,y,n,n,n,n,y,y,y,y,n,n republican,n,n,n,y,y,y,n,n,n,y,y,y,n,y,n,y democrat,n,?,y,?,n,y,y,y,y,y,y,n,?,?,y,y democrat,n,y,y,n,y,?,y,n,n,y,y,n,y,n,y,y republican,n,n,n,y,y,n,y,n,y,y,n,n,n,y,n,y democrat,n,n,y,n,n,n,y,y,y,y,y,n,n,n,y,y republican,n,n,n,y,y,y,y,n,n,y,n,y,n,y,y,y republican,n,n,n,y,y,y,n,n,n,y,n,y,y,y,n,y republican,y,n,n,y,y,y,n,n,n,y,n,y,y,y,n,n democrat,y,n,y,n,n,n,y,y,y,y,n,y,n,n,y,? republican,n,y,y,y,y,y,y,y,y,n,n,y,y,y,n,y democrat,n,y,n,n,n,y,y,n,y,n,y,n,n,n,y,y republican,n,n,y,y,y,y,y,y,y,y,n,y,y,y,y,y democrat,n,y,n,y,n,y,y,y,y,n,y,n,y,n,y,? republican,n,n,y,y,y,y,y,n,n,y,y,y,y,y,n,y democrat,n,y,y,n,n,y,y,y,y,y,n,?,n,n,y,y republican,y,n,y,y,n,n,n,y,y,y,n,n,n,y,y,y republican,n,n,n,y,y,y,n,n,n,n,n,y,y,y,n,n republican,n,n,n,y,y,y,n,n,n,n,n,y,y,y,n,n democrat,y,y,y,n,n,y,y,y,y,y,y,y,y,y,n,? republican,n,n,n,y,y,y,n,n,n,y,?,y,y,y,n,y democrat,y,n,y,n,n,y,y,y,y,y,n,n,y,n,n,y democrat,y,n,y,n,y,y,y,n,y,y,n,n,y,y,n,? democrat,y,y,y,n,n,y,y,y,y,y,y,y,y,n,n,y republican,y,y,n,y,y,y,n,n,n,y,y,n,y,n,n,n republican,y,y,n,y,y,y,n,n,n,n,y,n,y,y,n,y democrat,n,y,n,n,y,y,n,n,n,y,y,n,y,y,n,n democrat,y,n,y,n,n,n,y,y,n,y,y,n,n,n,n,? democrat,y,y,y,n,y,y,y,y,n,y,y,n,n,n,y,? democrat,n,y,y,n,n,y,y,y,n,y,n,n,n,n,y,y republican,n,y,n,y,y,y,n,n,n,n,n,n,y,y,n,y democrat,y,y,y,n,?,y,y,y,n,y,?,?,n,n,y,y democrat,y,y,y,n,?,n,y,y,y,y,n,n,n,n,y,? democrat,n,y,y,y,y,y,n,n,n,n,y,y,?,y,n,n democrat,n,y,y,?,y,y,n,y,n,y,?,n,y,y,?,y republican,n,y,n,y,y,y,n,n,n,n,n,y,y,y,n,y democrat,n,y,n,y,y,y,n,n,n,n,y,y,n,y,n,n democrat,y,?,y,n,n,n,y,y,y,n,y,n,n,n,y,y republican,n,y,n,y,y,y,?,?,n,n,?,?,y,?,?,? republican,n,n,n,y,y,y,n,n,n,n,n,y,y,y,n,y republican,n,n,n,y,y,y,n,n,n,n,n,y,y,y,n,y democrat,y,y,y,n,n,y,?,y,y,n,y,n,y,n,y,y democrat,y,y,y,n,y,y,y,y,y,y,y,n,y,y,n,? democrat,y,y,n,y,y,y,n,n,n,n,y,n,y,y,n,? democrat,y,y,y,n,y,y,n,y,y,y,y,n,n,n,n,y democrat,y,y,y,y,y,y,n,n,n,n,y,y,y,y,n,y democrat,y,y,n,n,y,y,n,n,n,n,y,y,y,y,y,n democrat,n,?,y,n,y,y,n,y,n,n,y,n,n,n,n,? democrat,y,y,y,n,y,y,n,y,y,n,y,n,n,y,n,? democrat,n,y,y,y,y,y,n,n,n,n,n,y,y,y,n,? democrat,y,n,y,n,n,n,y,y,y,?,y,n,n,n,y,? democrat,?,?,n,n,?,y,?,n,n,n,y,y,n,y,n,? democrat,y,y,n,n,n,n,n,y,y,n,y,n,n,n,y,n republican,y,y,n,y,y,y,n,n,n,n,y,y,y,y,n,y republican,?,?,?,?,n,y,n,y,y,n,n,y,y,n,n,? democrat,y,y,?,?,?,y,n,n,n,n,y,n,y,n,n,y democrat,y,y,y,?,n,n,n,y,n,n,y,?,n,n,y,y democrat,y,y,y,n,y,y,n,y,n,n,y,n,y,n,y,y democrat,y,y,n,n,y,?,n,n,n,n,y,n,y,y,n,y democrat,n,y,y,n,y,y,n,y,n,n,n,n,n,n,n,y republican,n,y,n,y,?,y,n,n,n,y,n,y,y,y,n,n republican,n,y,n,y,y,y,n,?,n,n,?,?,?,y,n,? republican,n,y,n,y,y,y,n,n,n,y,y,y,y,y,n,n republican,?,n,y,y,n,y,y,y,y,y,n,y,n,y,n,y republican,n,y,n,y,y,y,n,n,n,y,n,y,?,y,n,n republican,y,y,n,y,y,y,n,n,n,y,n,y,y,y,n,y republican,n,n,n,y,y,y,n,n,n,n,n,y,y,y,n,y democrat,y,n,y,n,y,y,n,n,y,y,n,n,y,y,n,y democrat,n,n,n,y,y,y,n,n,n,n,y,y,y,y,n,n democrat,y,n,y,n,n,y,y,y,y,n,n,y,?,y,y,y republican,n,n,n,y,y,y,n,n,n,n,n,y,y,y,n,n republican,n,n,n,y,y,y,n,n,n,n,y,y,y,y,n,y democrat,y,n,y,n,n,y,y,y,y,y,y,n,n,n,n,y republican,n,n,n,y,y,y,n,n,n,y,n,y,y,y,n,y republican,y,y,y,y,y,y,y,y,n,y,?,?,?,y,n,y democrat,y,y,y,n,n,n,y,y,y,n,n,n,n,n,n,y democrat,n,y,y,n,n,y,y,y,?,y,n,n,n,n,n,y republican,y,y,n,y,y,y,n,n,n,y,n,n,y,y,n,y democrat,y,y,y,n,n,n,y,y,y,y,y,n,y,n,n,y democrat,y,y,y,n,n,n,y,y,n,y,n,n,n,n,n,y democrat,y,y,y,n,n,n,y,y,y,n,n,n,n,n,n,y republican,y,y,y,y,y,y,y,y,n,y,n,n,y,y,n,y democrat,n,y,y,n,y,y,y,y,n,n,y,n,y,n,y,y democrat,n,n,y,n,n,y,y,y,y,n,y,n,n,n,y,y democrat,n,y,y,n,n,y,y,y,y,n,y,n,n,y,y,y democrat,n,y,y,n,n,?,y,y,y,y,y,n,?,y,y,y democrat,n,n,y,n,n,n,y,y,n,y,y,n,n,n,y,? democrat,y,n,y,n,n,n,y,y,y,y,n,n,n,n,y,y republican,n,n,n,y,y,y,y,y,n,y,n,y,y,y,n,y democrat,?,?,?,n,n,n,y,y,y,y,n,n,y,n,y,y democrat,y,n,y,n,?,n,y,y,y,y,n,y,n,?,y,y republican,n,n,y,y,y,y,n,n,y,y,n,y,y,y,n,y democrat,n,n,y,n,n,n,y,y,y,y,n,n,n,n,n,y republican,n,?,n,y,y,y,n,n,n,n,y,y,y,y,n,y republican,n,n,n,y,y,y,?,?,?,?,n,y,y,y,n,y republican,n,y,n,y,y,y,n,n,n,y,n,y,y,y,?,n This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,14 @@ # Data Science Workshop Demo and Lab This gist is for *Data Science and Machine Learning Using Python scikit-learn* workshop. The workshop is a part of the DataEngConf 2016, on April 7-8 2016, held at Galvanize, 44 Tehama Street, San Francisco, CA. The gist contains the following demo, lab, and data files. File |Contents ---- |-------- `titanic.ipynb` |Titanic Data Analysis IPython Notebook `titanic.csv` |Titanic Dataset `congress-lab.md` |Congress Voting Lab `congress.csv` |Congress Voting Dataset This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,892 @@ PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S 2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C 3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S 4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S 5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S 6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q 7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S 8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S 9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S 10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C 11,1,3,"Sandstrom, Miss. 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