Multi-agent system simulation: Quick Start with ZeroMQ [Python]

Created using Python 2.7 and ZMQ 4.2.1.

Recent work has brought me towards the direction of multi-agent A.I. systems. This was fairly challenging because I had no prior training in terms of multi-agent systems. In fact, whenever I had to code, I had always resigned to function-driven code; always shying away from object-oriented  environments.

Realizing that there was no escaping object-oriented programming this time, I quickly dove into it. Subway rides quickly became refresher sessions on classes and boot camps into multi-agent systems and communication.

The biggest question that I had was, how do multiple agents, whether hosted locally or in different systems, communicate with each other? Quick research brought me to four libraries. The codes are located in this GitHub repo.

  1. Threading Module
  2. SimPy (Discrete Event Simulation for Python)
  3. PyRo (Python Remote Objects)
  4. ZeroMQ (Distributed Messaging)

My method of going through with this project was to start writing code and just reassess the process when I start breaking stuff. Take note that I didn’t dive into all the capabilities of the first three libraries. It just so happened that ZeroMQ gave me what I wanted faster than the other three.

Threading Module

Python’s threading module is beautiful, especially when you get multiple threads working. Unfortunately, I ran into a wall when I needed to change the agent type. As you can see below, although the Basic agents changed into Better agents, the Better agent threads that were started did not run past the initial three processes. I also had the problem that this simulation might not show what I wanted: which was multiple independent agents communicating with each other. Since they were housed in threads of the same python instance, I don’t see how my goal could be realized.

SimPy (Discrete Event Simulation)

SimPy was pretty cool as well. I wasn’t able to dive really deep into it, but from what I initially saw in the tutorials, SimPy objects had to run in an environment. This is generally how A.I. agents run, which is, in an environment, but I had a problem with the fact that I need to run an environment for these agents to work with each other. I need the agents to run in multiple computers in the future. Also, I need a brokerless system where the agents communicate with each other without having a central “server” or environment to make them run. Here is the implementation of a code that came from the tutorial. The code introduces three cars and a refilling station with two slots. It’s basically a queueing system.

PyRo (Python Remote Objects)

This library was closer to what I needed than Threading and SimPy. It allowed me to run a server and three (3) agents. Truth be told, I was set in using this, but a few things still bothered me:

  • Will I be able to convert remote objects into servers as well? As I said, my goal is a brokerless system, where agents communicate from each other. I tried this but to no avail, it did not work. All communication had to go through the server/remote server.
  • Pyro4’s expose function, which exposes a remote object’s class’ variables or functions to the other instances, seems to not be working correctly. I might have been using it wrong. I doubt since some of the variables were accessible.
  • Communication was extremely easy… to tangle up.

There are a few things that I liked though.

  • Pyro has a nameserver, which allows agents to connect to a server or remote object using its name. This is extremely convenient, in my opinion, for situations where the agents have to dynamically connect to new servers or remote objects, since they can just call the name.
  • Daemon loop capabilities
  • Supports multiple python instances.

*GIF demonstration and code to follow

ZeroMQ

Now, this. Among the four libraries, ZeroMQ gave me what I really needed (Brokerless system); and it did so in a truly fashionable way. Communication among agents was easy because ZeroMQ’s communication style is in terms of patterns. The basic patterns that you could execute are the following:

  • PUSH-PULL: one-way communication. Commands can be executed this way.
  • REQ-REP: Request and reply communication. This allows agents to respond to another agent’s request. This basically starts a conversation between the agents.
  • PUB-SUB: Publish-Subscription communication. This allows an agent to continuously broadcast a message. Agents who are subscribed to a certain message will react to or get woken up whenever it gets pinged. Update streams, heartbeats, and wake-up signals come into mind when this type of comms is mentioned.

There are more patterns that can be used that would allow sending and receiving multiple messages simultaneously without locking the system and network, but even with these three patterns, it’s pretty doable and easy to set up multiple connection types between an agent and parallel instances of itself.

Quick Tips:

  • Messages can only be delivered in string or serialized format. Currently, I’ve only tried serializing a dictionary to json format (using simplejson). To my knowledge, pickles, c-pickles, and message packs are also allowed.
  • Agents can act as PUSHERS, PULLERS, PUBLISHERS, SUBSCRIBERS, REQUESTERS, AND REPLIERS (and other roles not mentioned above), all at the same time. This allows agents to communicate with each other without having to rely on a server to get the message across. If you are designing an agent to be more than of these types at the same time, you will have to assign different ports to each type of socket.
  • There are different types of connections. I’m currently using TCP port connections in my local machine. If I will have to deploy more agents, there has to be a way to assign, reassign, and kill ports dynamically.
  • It allows connections between agents that have been coded using different languages.
  • Running multiple processes of functions of a certain agent instance will not work, at least in my current implementation, due to overlapping port connections.
  • The best thing is, in my case, I only need to create one PY file for a specific agent type. I could run this code in different python instances and have them interact dynamically with each other, since they can take up different roles, as mentioned above.  This is pretty handy because it helps with code organization. This also allows an easier management and deployment of the code in multiple systems.

Below is a simple implementation of three Basic agents trying to establish who is the best among themselves. The faster the timer runs out, the faster an agent can become a ‘Better’ agent. Since the Better agent is the first one to communicate and receive replies from the Basic agents, he is deemed the Best agent. Afterwards, he sends a heartbeat to the two other agents to prevent them from thinking that there is no Best agent alive and start their countdowns again.

This article will be update as I learn more about ZeroMQ. Check the section below for the most recent updates. Drop me a comment if you have any questions as well.

Credits:

  1. ZeroMQ’s guide – Due to this library being updated over and over again, most of the code that I saw outside of the site and number 2 below were outdated. Understanding this guide and thinking of how people think and communicate helped me become accustomed to ZeroMQ’s patterns.
  2. PyZMQ’s docs – Python bindings of ZMQ.

Classifying a Company’s True Earnings Quality using Text Analytics and Machine Learning on S&P Proxy Statements’ Compensation Discussion and Analysis [R, Python]

This was submitted as a project for my Text Analytics class in my MS Business Analytics program. The original title is “Text Analytics on the Compensation Discussion and Analysis of S&P 1500 Proxy Statements. My other teammates are Minglu Sun, Jiawen Zhou, and Yi Luo. This project was done for educational purposes only. Click the photos to enlarge. Check out the GitHub page for the files and data set. 

Problem Statement

The purpose of this study is to explore whether the sentiment, structure, and contents of a company’s Proxy Statement Compensation Discussion and Analysis (CD&A) reflects the company’s real financial performance in terms of the relationship of Earnings per Share and Operating Cash Flow per Share

Rationale

  • Public companies submit C-level management compensation reports to SEC every year. The Compensation Discussion and Analysis (CD&A) section discloses all material elements of the company’s executive compensation programs and provides the appropriate reasoning as to why the C-suite are being paid their respective salaries.
  • The compensation report is highly sensitive and is required to be explained with utmost transparency. In an attempt to standardize transparency in the document, in early 2017, SEC proposed rules and regulations that will require companies to disclose the relationship between executive pay and a company’s financial performance.
  • That being said, whether the Compensation Discussion and Analysis reflects the company’s real financial performance or not needs to be tested.

Hypothesis

In this project, we assumed that the more positive a company’s proxy statement’s CD&A was written, the better the earnings quality of a company is in a given fiscal year.

According to Investopedia, Two financial indicators are being used to present whether companies’ earnings with high quality or low quality. A company has high quality earnings if it is generating more cash than is reported in the income statement. Earning quality is low if the company’s statements are not showing the negative operating results of the company. True cash operating results are also overstated.

  • High quality earnings: Earnings Per Share (EPS) > Operating Cash Flow Per Share (CFS)
  • Low quality earnings: Earnings Per Share (EPS) < Operating Cash Flow Per Share (CFS)

Data Description

For this project, three data sets were collected:

  1. Randomly selected 1,500 companies’ S&P Proxy Statements’ Compensation Discussion and Analysis (CD&A) from the U.S.  SEC EDGAR System.
  2. Company performance (Earnings Per Share and Cash Flow Per Share) using the Intrinio Financial Marketplace API.
  3. Ticker and registered company industries from Google Finance.

In addition, two popular sentiment lexicons were selected for the sentiment analysis portion: Bing Liu’s sentiment dictionary and LoughranMcDonald Master Dictionary, which was specifically developed for Tim Loughran and Bill McDonald’s paper in the Journal of Finance entitled “When is Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks” (2011).

Document Structural Dimension

  • Although the SEC did not rule out the structure of the Proxy Statement, most of the companies share similar structure of the statement, as well as the Compensation Discussion and Analysis part.
  • In general, the first paragraph of the CD&A is the introduction, which briefly introduces what the content is included in this part.
  • The second paragraph is the Executive Summary. A large number of the companies disclose the current year financial performance in the Executive Summary, making it the important section for sentiment information. Most of the positive or negative words and phrases are extracted from the Executive Summary.
  • The rest of the Compensation Discussion and Analysis are detailed descriptions of the compensation policy, subcategories of the compensation, and the approval from the compensation committee. A few companies explain the compensation decisions in these detailed compensation components, which reveal the sentiment information.

Document Content Dimension

  • The documents share the characteristics of public financial statements. Most of the sentences analyze and compare numeric values, which represent financial performance.

Methodology

Data Preprocessing

  • Text data was extracted from the CD&As. It underwent cleaning, which involved removal of punctuations and special characters.
  • Domain-specific lexicon creation. In the process, positive and negative words, phrase, and templates were extracted from 200 of the 500 documents. In the process, positive and negative words, phrase, and templates were extracted.
    • Templates:
      • e.g. an <increase>/<decrease> of <amount> from <number>/<year>
      • <metric> <increased>/<decreased> <amount> over/compared to <year>
Domain-specific lexicon sample

Domain-specific lexicon sample

  • The team simulated “expertise” and classified the 200 documents into positive or negative performance/sentiment.

Sentiment Analysis

Feature-level Analysis

Feature-level Sentiment Analysis model

  • Polarity-based sentiment analysis was conducted using the two publicly available lexicons mentioned above.
  • Due to inadequate results, the team decided to create a new domain-specific lexicon that will hopefully produce a better result.
  • To complement the sentiment analysis, IBM Tone Analyzer was used to acquire 13 tonal dimension results for each company’s CD&A.

 Document-level Analysis

Document-level Sentiment Analysis Model

  • Using the “expert” classifications of the 200 labeled data and the domain-specific lexicon as the feature set, a term-document matrix data set, containing the quantity/existence of each feature in all the documents (500 in total, was created.

Term-Document Matrix

  • Using a Neural Network, the remaining 300 documents were classified into positive or negative sentiment classes.

Classification of Earnings Quality

  • Considering the sentiment classification from the polarity-based sentiment analysis model, using the domain-specific dictionary and the tonal information as predictors and the earning quality as the target variable, four scenarios were used and subjected to multiple classification models (random forest, neural network, and logistic regression).
  • The following scenarios were tested:
    • Scenario 1 : Financial Performances ~ CD&A Tones
    • Scenario 2: Financial Performances ~ CD&A Sentiment
    • Scenario 3: Financial Performances ~ CD&A Tones + Sentiment
    • Scenario 4: Financial Performances ~ Top 5 Predictor Importance (Tone + Sentiment)

Results and Discussion

Sentiment Analysis

Sentiment analytics, in this project, was approached in two ways: feature-level analysis by using polarity-based classification models and document-level analysis using document classification.

Feature-level Analysis

  • Feature-level sentiment analysis is initially conducted with two dictionaries: Bing Liu’s Lexicon and the Loughran McDonald Master Lexicon, which focuses on financial concepts and finance-driven directional phrases.
  • Running these dictionaries into a polarity-based sentiment analyzer (netting of counts of positive and negative words based on existence produced very bi-polar results.

Bing Liu and LonghranMcDonald Sentiment Results

  • Due to the unsatisfactory results of these dictionaries, it became clear that there was a need to use a more domain-specific lexicon.
  • Since such as dictionary is nonexistent, the we decided to create one by reading 200 documents and extracting positive and negative words, phrases, and templates.
    • For instance, positive dictionary include “strong performance”, “outperformed”, “exceeding our target”, “revenue increased”, etc.
    • The negative dictionary included “decrease”, “slow down in ”, “reduction”, “did not achieve”, etc.
    • In the process, each document is categorized as positive or negative. This serves as input in the document-level approach.
  • Surprisingly, the new dictionary classifies 487 documents as positive, 1 as negative, and the remaining 12 as neutral.

Total Polarity-based Sentiment Results

  • The accuracy of the model using the domain-specific dictionary is  67%.

Document-level Analysis

  • Using the classifications generating from the domain-specific dictionary creation phase, classification models were used to determine the sentiment class of the remaining 300 unlabeled documents.
  • Using the words and phrases in the created dictionary as predictors to sentiment, a neural network model with an accuracy of 57.89% was created. That being said, the it was decided that the classifications from the polarity-based model that used the domain-specific dictionary will be used as input for the succeeding steps.

Evaluation of Document-level Sentiment Analysis model

Tonal Analysis

Input data also included tonal results computed by the IBM Tones Analyzer. The 13 dimensions extracted are anger, disgust, fear, joy, sadness, analytical, confident, tentative, openness, conscientiousness, extraversion, agreeableness, and emotional range.

Sample Tonal Results

To give a better idea of how tonal results performed throughout the company list, we decided to aggregate results up to the industry level.

Slice of the Industry-level Tonal Results

  • Telecommunication services industry’s compensation discussion and analysis has the highest joy value, which is 0.41.
  • Basic Materials, Energy, and Industrials are the three industries share the same highest sadness value, which is 0.41.
  • Compare to sadness and joy, tentative tone value is less obvious. The radar chart above is a slice of the tonal analysis that contains only three tones.

Classification of Earnings Quality

Model Evaluation

  •  Among all the created models, the random forest model in scenario 3 produces the highest accuracy (83%), precision (0.8), recall (0.84), and F-score (0.8).

Conclusion

According to the classification results, CD&A documents with positive sentiment score will be more likely to have high earning ability, which is characterized by a higher Earnings per Share compared to the company’s Cash Flow per Share.

In addition, there are no significant difference in tone score and sentiment score among different industries.

Implications

  • The domain-specific lexicon of Compensation Discussion and Analytics will assists the users and stakeholders of the Proxy Statement to recognize positive and negative features, and enables them to make effective and efficient decisions.
  • Since the Compensation Discussion and Analytics shares the characters of financial statements, the dictionary can also be applied to analyze other financial statements.

Limitations and Future Direction

  • The syntactic template has not been matched to the text content and loss some of the features.
  • Secondly, the CD&A prefers to use positive words and phrases and avoid using negative expressions. Even though some of the companies in the negative situation in this year, the description in the discussions seems to be positive. Therefore, the positive frequency of featured words and phrases are higher than the actual number.
  • Thirdly, the data records from the original training set are imbalanced, there are far more positive documents than the negative class. A model that uses a balanced dataset can be created in the future.
  • Also, other financial performance parameters could be used as the target variables instead of cash flow per share or earning per share.

References

Exploring the Association of Movie Trailer Performance on YouTube and Box Office Success using Neural Net, Python, and R

This was submitted as a project for my Big Data Analytics class in my MS Business Analytics program. The original title is “Exploring the Association of Movie Trailer Performance on YouTube and Box Office Success”. My other teammates are Yi Cai, Michael Friscia, and Zheyu Tian. This project was done for educational purposes only. Click the photos to enlarge. Check out the GitHub page for the files and data set. Due to policies of thenumbers.com regarding their data, that particular data set won’t be uploaded.

UPDATE: If you scroll below, you will see that the final accuracy was 82.55%. Using genetic algorithms and a Sklearn implementation, the accuracy was improved to 98.66% (with a final generation average accuracy of 92.28%). Check out the code in this GitHub repo.

Problem Statement

The purpose of this study is to determine if there is a correlation between the performance of trailers on YouTube and Hollywood movie sales.

Project Significance

  • By evaluating important predictors from YouTube viewers, studios and agencies can create and publish movie trailers on YouTube more efficiently, thus:
    • driving box office ticket sales domestically and globally
    • generating more revenue
  • Trailer performance can be focused on and improved if it shows that there is a correlation to boxoffice/post-show sales

Data Collection

  • Data was collected from YouTube, using its proprietary API, and from thenumbers.com
    • Youtube – trailer performance and comments
    • thenumbers.com – Movie Box Office data
  • 32.4GB (when comments are expanded into 1 line per comment)
  • 1,713 movies
  • 5,244 trailers
  • 2,979,511 comments

Youtube Data

Variable Selection

  • The ROI variable had to be created.

Variables selected

Hypothesis and Rationale

  • There is a positive correlation between Youtube movie trailer performance indicators  and Box office performance/Video Sales.
    • Rationale: “Likes” = Sales
  • There is a positive correlation between Movie trailer comment sentiments and Box office/Video Sales  performance.
    • Rationale: If trailers are viewed in a positive manner, then people will be more likely to watch the movie.

Conceptual Model

  • After data extraction using Python, data was transformed using Python. Output files were CSV and TXT files.
  • Three sentiment models were implemented in the project: polarity-based sentiment models by using Bing Liu’s and Harvard IV-4 dictionaries, and Naive Bayes Classifier: NLTK Sentiment model.
    • To process part of the sentiment analysis, Apache Spark was used.
  • The sentiment scores were also used to help identify the ROI of each movie using a neural network model.

Project Conceptual Model

Results and Discussion

Variable Correlation Test

The graph, which was generated by R, shows the correlations between the independent variables and dependent variables.

There are three main main conclusions based on the graph:  

 1.The graph demonstrated a positive correlation among count Views, Count Comments, and Likes/Dislikes.

2. The graph was also used to test the hypotheses regarding the movie trailer features and movie performance which assumed that the movie trailer comment counts/ Movie Trailer Likes and Movie Box Office are positively correlated.

 3. Unfortunately, three sentiment models have little correlation with the Box Office Data (eg. ROI), which means that the initial hypothesis wasn’t proved. Two feature-based sentiment models have negative correlations with: Count Views, Count Comments, Likes/Dislikes.

Time Series Analysis

  • It was interesting to see that for 2008, even though with the financial crisis, overall ROI turned out to be good.
  • Another interesting finding is that ROI continuously decreased after 2008.

Sentiment Analysis

Two models were implemented for sentiment analysis.

  • a polarity-based model using Bing Liu’s and a Harvard dictionary, which nets the counts of positive and negative words that can be found in each comment, and
  • the NLTK Sentiment Analyzer using the Vader dictionary, which is a rule-based approach
  • Scores were scaled and centered to zero to maintain positive scores > 0 and negative scores < 0. The scale is [-1,1].

  • Comparing the performance of the three models, the Polarity-based models gravitated towards negative sentiment, which could be explained by the internal structure of the dictionaries used; meaning, if there were more negative than positive words, most likely there will be a higher chance of a higher negative-word count.
  • For the NLTK Sentiment Analyzer, results showed more positive sentiment towards the comments.

Sentiment Analysis – Movie Studios

  • Based on the Harvard sentiment dictionary, Paramount Vantage has the lowest average sentiment score whereas Weinstein Company has the highest.
  • The Vader sentiment sentiment dictionary determined that Apparition has the highest average sentiment score while Focus/Gramercy has the lowest sentiment average score.
  • Bing Liu sentiment dictionary predicted that Freestyle Releasing and Apparition have the lowest and highest average sentiment score, respectively.

Sentiment Analysis – Genre

  • When evaluating the Bing Liu and Harvard dictionaries, Romantic Comedies and Documentaries have the highest and lowest average sentiment score respectively.
  • Interestingly, for the NLTK Analyzer, the Concerts and Performances genre has the lowest average sentiment score, while Romantic Comedy has the highest score.

Clustering (to follow)

Predicting Box Office ROI Performance using Neural Net

  • ROI performance was classified using four bins:
    • Poor (less than the 25% quantile)
    • Passing (between 25% and 50% quantile)
    • Ok (between the 50% and 75% quantile)
    • Great (above the  75% quantile)
  • Neural Net implemented using R
  • ROI Performance ~ countsComments + countsViews + Ratio_of_Likes_and_Dislikes + ProdBudget + genre + MPAArating + MovieStudio + BingLiuSentiment + HarvardSentiment + VadeSentiment
  • Model Accuracy = 82.55%

    Neural Net Model Results

Conclusion

  • Due to the success of the neural network model, companies now have the ability to accurately predict the ROI of their movies, specifically with the use of the number of YouTube comments, ratio of likes and dislikes, and their sentiment scores from the three models.
  • With the hypotheses predicted for the research, there is a higher probability of Box Office success which would then in return generate a higher ROI for movie studios and production companies. 
  • Although the sentiment results are different among the three dictionaries, this implicates that some dictionaries used in the models view more neutral words as negative or positive.
    • The best alternative methods  to predict the sentiment of YouTube comments in movies are to use domain-specific dictionaries and the application of  machine learning classifiers paired with a sample comment-sentiment data set. 

Scope and Limitations

  • There are many popular websites and applications that can be used to comment on trailers or movies, such as Rotten Tomatoes, Facebook, Twitter and so on. However, in this case, Youtube is the only trailer source used.

  • Trailers are not the only factors that impact box office and video sales. Other factors such as advertisements,the actors, and  the competition of other movies being released at the same time can have an effect on the movie’s box office sales. However, these factors are not included in this study. Further studies could be conducted with those variables included.

Reference

Love in the Fastlane – Predicting Success in Speed Dating using Logistic Regression and R

This was submitted as a project for my Statistical Methods and Computation class in my MS Business Analytics program. The original title is “Love in the Fastlane: Success in Speed Dating”. My other teammate is Ruoxuan Gong. This project was done for educational purposes only. Click the photos to enlarge. Check out the GitHub page for the files and data set.

Problem Statement

The purpose of this study is to determine if speed dating outcomes can be predicted, and if yes, what are the most important factors that would help speed dating participants successfully match with each other

Methodology

Data Cleaning and Pre-processing

  • 8,369 rows containing speed dating round and participant information in one table
  • Table was separated into two tables: round-specific data (round condition and relative data) and participant data (demographics and interests)
  • Has a big amount of double-counted rows:
    • Wave ID 1 with Participant A and Partner D
    • Wave ID 1 with Participant D and Partner A (double-count and removed)
  • After removal of double-counts, nrow = 4,184.
  • Columns with a lot of missing data were removed.
  • Domain knowledge used to produce initial set of variables
  • Rows with missing data were removed; new nrow = 3,377
  • All Participants are women and all partners are men

Hypothesis

It is possible to predict, to a certain level of confidence, the outcome of a speed dating round(match or no match) by analyzing and taking into consideration the different factors during the round itself.

Variable Selection

  • Dependent Variable: Match (1/0)
  • Independent Variables: Round-specific data, difference in preference ratings (attractiveness, sincerity, intelligence, fun(funny?), ambitious, and shared hobbies), ratings for and by the participant

Tool and Model Selection

  • Tool: R was the main tool used for this project.
  • Model: Logistic Regression
    • Target is binary (Match = 1/0)
    • Assumptions:
      • Explanatory variables are measured without error
      • Model is correctly specified (No important variables are omitted, extraneous variables are excluded)
      • Outcomes not completely separable
      • No outliers
      • Variables should have little or no multicollinearity (VIF test)
      • Observations are independent (no time series, no in group data)
      • Sample size = n = at least 10 observations for each outcome (0/1) per predictor

Results and Discussion

Descriptive Analysis – Overall Data set

  • Here, we could see that the ages of the participants typically ranged from the 20s to 40s, with a few outliers
  • Ages 20-30: mixed ratings for almost all interests
  • Ages 30-40: Trend of rating goes higher for reading, movies, music, museums, and art

Distribution of Interests by Age by Gender

 

  • In the chart below, data shows that women are more interested in theater, art, and shopping, while men like gaming more.
  • Both genders share interests in dining, reading, movies, and music

  • In terms of preference, it would seem that women prefer intelligence over ambition and attractiveness
  • Men, on the other hand, prefer attractiveness over ambition and shared hobbies

Descriptive Analysis – Match = 1 Scenarios

  • We also wanted to take a look at what the average man and woman in match = 1 scenarios looked like
  • From the looks of it, men who joined were generally older than women
  • Also, men expected to be happier in the event and compared to women
  • Surprisingly, most joined the event to
  • “have a fun night out”, “meet new people”, or “try it out”.
  • Only a very few joined for “romantic reasons”

Age, Goal, and Expected Happiness Comparison for Match = 1 Scenarios

  • There were high ratings from both genders for exercise, dining, hiking, music, concerts, and movies
  • There were small variances for museums, art, clubbing, reading, and TV
  • Large variances were seen for theater, shopping, and yoga
  • Lastly, both had low ratings for gaming (I don’t understand why… lol)

Interest Rating Comparison by Gender

  • Very low variances were observed in the preferences of men and women
  • Might suggest that a match tends to happen when both participant and partner have the same level of preference, no matter what level it is

Preference Comparison by Gender

  •  This is how an average man and woman in a match = 1 scenario would look like in terms of their interest and preference ratings

Predictive Analysis – Logistic Regression

  • Going to predictive analytics, the first thing that we did was to check the distribution of the variables. Although multivariate normality isn’t an assumption for logistic regression, having normal variables help make the model stable.
  • We selected the variables that were not normal in shape and decided to apply transformations on them.
  • Of the three, square root transformation produced the most-normal-looking transformations. This prompted us to pick this over the original and log transformed variables.

  • The initial model produced the following results:
    • Accuracy = 83.41%
    • Recall = 0.244
    • precision = 0.659
    • AUC = 0.849
    • AIC = 1,945.32

  • We believed that this model could still be improved. That being said, we chose to apply a stepwise function on it.
  • After around 12 iterations, our round variables were stripped down to 13 (from 25)
  • AIC decreased to 1,929.27 (from 1,945.32)
  • Although the ROC curve didn’t seem to change, the AUC score increased from 0.84989 to 0.84996
  • The equation below represents the model in its current form

  • To satisfy one of the assumptions (the model should be correctly specified, meaning there are no important variables that are omitted and all extraneous variables should be excluded), we removed all the variables that had high p-values (>0.5, except for the ones with the “.”s)
  • By removing the extraneous variables, we were able to make the coefficients of the remaining variables more reliable
  • The new model resulted to the equation below:

  • The improved model has an precision of 0.6739. In addition, the final AUC score is 0.847.
  • It also shows an improvement in the model’s total True Negatives.

Assumption Testing

  • Explanatory variables are measured without error
    • Limitation of using third-party data. We assumed this to be true.
  • Model is correctly specified (No important variables are omitted, extraneous variables are excluded)
    • Demonstrated above.
  • Outcomes not completely separable
    • In R, the glm() function will not work if this was not true
  • No outliers
  • Observations are independent
    • no time series, no in group data
  • Sample size = n = at least 10 observations for each outcome (0/1) per predictor
    • Our nrow more than covers this requirement
  • Variables should have little or no multicollinearity
    • In order to test for multicollinearity, we ran the Variance Inflation Factor test.
    • Having a VIF of <5 means that the model has low or no multicollinearity. That being said, since the VIFs of our variables are <2, then we can say that the level of multicollinearity in the model is negligible, at best.

Conclusion

In conclusion, a logistic regression model can be used to predict the outcome of speed dating rounds. It can be represented using the formula below:

Implications

  • Attractiveness has a very big impact on producing a successful match, especially when females (participants) are perceived to be more attractive.
  • For females, meeting someone who brings fun to them increases the chance of getting a match.
  • In other words, males (partners) care about attractiveness more, and females prefer someone who has a sense of humor.

However, factors like having an ambitious personality has a negative impact on a successful match.

Scope and Limitations

  • Many valuable variables were excluded because of missing values
  • Given the reality of using third party data, information in terms of location and time of data collection is limited, leaving us without any knowledge if there are any biases in the results

References

[1] Donald, B. (2013, May 6). New Stanford research on speed dating examines what makes couples ‘click’ in four minutes. Retrieved from http://news.stanford.edu/news/2013/may/jurafsky-mcfarland-dating-050613.html

[2] Data set: http://www.stat.columbia.edu/~gelman/arm/examples/speed.dating/

Google Slides

Predicting the Winner of March Madness 2017 using R, Python, and Machine Learning

This project was done using R and Python, and the results were used as a submission to Deloitte’s March Madness Data Crunch Competition. Team members: Luo Yi, Yufei Long, and Yuyang Yue. Check the GitHub for the code.

Of the 64 teams that competed, we predicted Gonzaga University to win. Unfortunately, they lost to University of North Carolina.

Methodology

  1. Data transformation
  2. Data exploration
    • Feature Correlation testing
    • Principal Component Analysis
  3. Feature Selection
  4. Model Testing
    • Decision Tree
    • Logistic Regression
    • Random Forest
  5. Results and other analysis

Data Transformation

The data that was used to train the initial model was from a data set that contained 2002-2016 team performance data, which included statistics, efficiency ratings, etc.,  from different sources. Each row was a game that consisted of two teams and their respective performance data. For the initial training of the models, we were instructed to use 2002-2013 data as the training set and 2014-2016 data as the testing set. After examining the data, we debated on what would be the way to use it. We finally decided on creating new relative variables that would reflect the difference/ratio of team 1 and team 2’s performance. Feature correlation testing was also done during this phase. The results supported the need for relative variables.

Features Correlation Heatmap (Original Features)

Data Exploration

After transformation, feature correlation testing was repeated. This time, results were much more favorable. The heat map below shows that the correlation between the new variables is acceptable.

Features Correlation Heatmap (New Features)

Principal Component Analysis was also performed on the new features. We hoped to show which features were the most influential, even before running any machine learning models. Imputation was done to deal with missing values. The thicker lines in the chart below signify a more influential link to the 8 new discriminant features. This, however, was used to understand the features more and wasn’t used as an input for all the models.

Feature Selection by PCA

Feature Selection

For this project, we opted to remove anything (aside from seed and distance from game location) that wasn’t a performance metric. Some of the variables that were discarded were ratings data since we believed that they were too subjective to be reliable indicators.

Model Testing

We used three models for this project: Decision Tree, Logistic Regression, and Random Forest.

Decision Tree – Results were less than favorable for this model. Overfitting occurred and we had to drop it.

Random Forest (R) – We decided to use the Random Forest model for 2 different reasons: the need to bypass overfitting restrictions and its democratic nature.

Predictor Importance

  • OOB Estimate of error rate: 26.9%
  • Error reduction plateaus at approx. 2,600 trees
  • Model Log-loss: 0.5556
  • Chart Legend:
    • Black:  Out-of-bag estimate of error rate
    • Green and Red: Class errors

Forest Error Performance

Logistic Regression (Python) – From PCA analysis and Random Forest Model, 5 features were selected for this model. 

Features Selected for Logistic Regression

Results and Other Analysis

Summary of Results

Running them against the testing set, we were able to get a higher accuracy for the Random Forest model. Log loss, which was also one of the key performance indicators for the competition, was relatively the same for the 2 models. That being said, Random Forest was chosen to run the new 2017 march madness data.

As previously mentioned, we had predicted Gonzaga University to win the tournament. We came really close though. It made a lot of sense because, compared to the other teams, Gonzaga was a frequent contender in March Madness.

One of the more interesting teams this season was the cinderella team, South Carolina. They had gone against expectations, and this is why we decided to analyze their journey even further.

In the 1st round, we were able to correctly predict that South Carolina was going to win. However, because we were using historical data, it was obvious that we were going to predict them to lose in the next stages, especially since they were going against stronger teams. Despite “water under the bridge” data, they were able to reach the Final 4.

Cinderella Team Win Rate by Stage

One of the questions that we wanted to attempt to answer was why they kept on winning. What was so different this year that they were able to surprise everyone?

One reason that we speculated about was the high performance of one of South Carolina’s players, Sindarius Thornwell. In the past years, he was averaging 11-13 pts per game. This year, he was dropping 21.4 pts per game. Moreover, in his last 5 appearances, his was able to increase this stat to 23.6 pts per game. Looking at the score difference of South Carolina’s games in March Madness, it is evident that he was very influential in the team’s success. One could even say that without his 23.6 pts per game, the turnout of their campaign would’ve been different. But hey, that’s just speculation.

Score Difference for Cinderella Team Matches

 

Sindarius Thornwell March Madness Stats

 

Solving the Greenhouse Gas Problem through Sustainable Meat Consumption (Watson Analytics)

 

This is my team’s official entry to the 2017 Watson Analytics Global Competition. Beyond our hope to win the competition is the hope that our recommendations will be put to use by policy makers in the different countries. We believe that this is something that can make a difference. Team members are Ruoxuan Gong and Liyi Li.

Abstract

People are rarely aware of meat consumption’s contribution to greenhouse gas emissions. The purpose of this study is to utilize IBM Watson Analytics to identify relationships among meat consumption, greenhouse gas emission, and potential thermal depolymerization by-products from meat production funnels. Thorough data collection, data preprocessing, and data analysis, using both descriptive and predictive analytics, were conducted. As a result, three solutions: policies to optimize meat consumption, transformation of solid waste to sustainable by-products, and social media methods to increase people’s awareness have been proposed in this project.

The dashboard and research-based data-driven golden information can be used by environmental policy makers, business owners, and the public to exponentially make meat consumption more sustainable in the long run. Network effects can be expected from the improvement of public awareness.

Methodology

  1. Data collection from OECD, FAO, and other sources.
  2. Data processing to relate meat production & consumption data with greenhouse gas emission data
  3. Variable Selection
  4. Data Analysis
    • Chart creation
    • Dashboarding
    • Simulation of thermal depolymerization by-product conversion
    • Retrospective Analysis
    • Social Media Awareness Analysis
  5. Conclusion and Recommendations

Data set transformation of FDNY’s Fire Incident Dispatch data using Pandas (Python)

This quick data set transformation was done for one of my current analytics projects. We decided to study and run models on FDNY’s public fire incident dispatch data from NYC Open Data. It’s an on-going project and the results will be posted at the end of the semester.

The goal of this transformation is to produce a data set that has the following filters and conditions:

  • Remove all row with empty values
  • Filter out the year 2017
  • Split out date strings to convert to Python readable date formats
  • Filter out “Medical Emergencies” and “Medical MFAs” from Incident_Classification_Group
  • Remove insignificant columns
  • Create drill down variables for some of the columns (Incident_Datetime, Incident_Borough, etc.)
  • Replace Police Precinct category with a count of the number of police precincts in each zip code

The end result decreased rows from 1.8M to 800K+.

The transformation was done on Python. The code can be seen below.

Original data set: https://data.cityofnewyork.us/Public-Safety/Fire-Incident-Dispatch-Data/8m42-w767/data

Initial Transformation:

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
import csv
import pandas as pd
import numpy as np
from datetime import date, time, datetime

rowlist = []
totallist = []
countrows = 0
#PART 1: remove all rows with empty values
# data = pd.read_csv('Fire_Incident_Dispatch_Data.csv', sep = ',', dtype = {'INCIDENT_RESPONSE_SECONDS_QY':np.int64, 'INCIDENT_TRAVEL_TM_SECONDS_QY': np.int64, 'ENGINES_ASSIGNED_QUANTITY': np.int64, 'LADDERS_ASSIGNED_QUANTITY':np.int64,'OTHER_UNITS_ASSIGNED_QUANTITY': np.int64})
# # data.dropna().to_csv('fireincidentdatasetnoblank.csv')
# data = data.dropna()


#PART 2: data set transformation
#get month, year, day, daydate, and time
		#1/01/2013 12:00:37 AM - format
def convert_datetime(csv_datetime):

	DATETIME_SPLIT = csv_datetime.split(' ')
	DATE_SPLIT = DATETIME_SPLIT[0].split('/')
	MONTH = int(DATE_SPLIT[0])
	DAYDATE = int(DATE_SPLIT[1])
	YEAR = int(DATE_SPLIT[2])
	dateformat = date(YEAR,MONTH,DAYDATE)
	WEEKDAY_INDEX = dateformat.weekday()

	#format INCIDENT date
	if WEEKDAY_INDEX == 0:
		WEEKDAY = 'MONDAY'
	elif WEEKDAY_INDEX == 1:
		WEEKDAY = 'TUESDAY'
	elif WEEKDAY_INDEX == 2:
		WEEKDAY = 'WEDNESDAY'
	elif WEEKDAY_INDEX == 3:
		WEEKDAY = 'THURSDAY'
	elif WEEKDAY_INDEX == 4:
		WEEKDAY = 'FRIDAY'
	elif WEEKDAY_INDEX == 5:
		WEEKDAY = 'SATURDAY'
	else:
		WEEKDAY = 'SUNDAY'

	#format time
	TIME_SPLIT = DATETIME_SPLIT[1].split(':')
	HOUR = int(TIME_SPLIT[0])
	MINUTE = int(TIME_SPLIT[1])
	SECOND = int(TIME_SPLIT[2])
	

	if DATETIME_SPLIT[2] == 'AM':
		if HOUR in [12,1,2,3,4,5]:
			QTR_DAY = 'Early Morning'
			if HOUR == 12:
				HOUR_PYTHON = 0
			else:
				HOUR_PYTHON = HOUR
		else:
			QTR_DAY = 'Morning'
			HOUR_PYTHON = HOUR
	else:
		if HOUR in [12,1,2,3,4,5]:
			QTR_DAY = 'Afternoon'
			if HOUR == 12:
				HOUR_PYTHON = HOUR
			else:
				HOUR_PYTHON = HOUR + 12
		else:
			QTR_DAY = 'Evening'
			if HOUR == 12:
				HOUR_PYTHON = HOUR
			else:
				HOUR_PYTHON = HOUR + 12
	timeformat = time(HOUR_PYTHON, MINUTE, SECOND)
	datetimeformat = datetime(YEAR,MONTH,DAYDATE,HOUR_PYTHON,MINUTE,SECOND)
	datetimelist = [YEAR,MONTH,DAYDATE,WEEKDAY,QTR_DAY,str(timeformat),str(datetimeformat)]
	return datetimelist

#process and transform data
with open('fireincidentdatasetnoblank.csv', 'rb') as csv_in:
	myreader = csv.reader(csv_in, delimiter = ',')
	next(myreader) #skips column headers

	for row in myreader:

		INCIDENT_DATETIME = row[2]
		INCIDENT_BOROUGH = row[6]
		ZIPCODE = int(float(row[7]))
		POLICEPRECINCT = int(float(row[8]))
		ALARM_SOURCE_DESCRIPTION_TX = row[13]
		HIGHEST_ALARM_LEVEL = row[15]
		INCIDENT_CLASSIFICATION = row[16]
		INCIDENT_CLASSIFICATION_GROUP = row[17]
		DISPATCH_RESPONSE_SECONDS_QY = row[18]
		FIRST_ASSIGNMENT_DATETIME = row[19]
		FIRST_ACTIVATION_DATETIME = row[20]
		FIRST_ON_SCENE_DATETIME = row[21]
		INCIDENT_CLOSE_DATETIME = row[22]
		VALID_INCIDENT_RSPNS_TIME_INDC = row[24]
		INCIDENT_RESPONSE_SECONDS_QY = row[25]
		INCIDENT_TRAVEL_TM_SECONDS_QY = row[26]
		ENGINES_ASSIGNED_QUANTITY = row[27]
		LADDERS_ASSIGNED_QUANTITY = row[28]
		OTHER_UNITS_ASSIGNED_QUANTITY = row[29]

		#compute for total_resource_qty
		TOTAL_RESOURCE_QTY = int(str(ENGINES_ASSIGNED_QUANTITY)) + int(str(LADDERS_ASSIGNED_QUANTITY)) + int(str(OTHER_UNITS_ASSIGNED_QUANTITY))

		INCIDENT_DATETIME_LIST = convert_datetime(INCIDENT_DATETIME)
		FIRST_ASSIGNMENT_DATETIME_LIST = convert_datetime(FIRST_ASSIGNMENT_DATETIME)
		FIRST_ACTIVATION_DATETIME_LIST = convert_datetime(FIRST_ACTIVATION_DATETIME)
		FIRST_ON_SCENE_DATETIME_LIST = convert_datetime(FIRST_ON_SCENE_DATETIME)
		INCIDENT_CLOSE_DATETIME_LIST = convert_datetime(INCIDENT_CLOSE_DATETIME)
		#datetimelist = [YEAR,MONTH,DAYDATE,WEEKDAY,QTR_DAY,timeformat,datetimeformat]
		#Compute for Incident_Resolution_Sec
		INCIDENT_RESOLUTION_SEC = datetime.strptime(INCIDENT_CLOSE_DATETIME_LIST[6],'%Y-%m-%d %H:%M:%S') - datetime.strptime(INCIDENT_DATETIME_LIST[6],'%Y-%m-%d %H:%M:%S')

		if VALID_INCIDENT_RSPNS_TIME_INDC == "Y":
			if INCIDENT_DATETIME_LIST[0] == "2017":
				pass
			else:
				#INDEPENDENT VARIABLES
				#datetime drilldown
				rowlist.append(INCIDENT_DATETIME_LIST[6])
				# rowlist.append(INCIDENT_DATETIME_LIST[0])
				# rowlist.append(INCIDENT_DATETIME_LIST[1])
				# rowlist.append(INCIDENT_DATETIME_LIST[2])
				# rowlist.append(INCIDENT_DATETIME_LIST[3])
				# rowlist.append(INCIDENT_DATETIME_LIST[4])
				# rowlist.append(INCIDENT_DATETIME_LIST[5])
				# #location drilldown
				# rowlist.append(INCIDENT_BOROUGH)
				rowlist.append(ZIPCODE)
				#policeprecinct
				rowlist.append(POLICEPRECINCT)
				#alarmsource
				rowlist.append(ALARM_SOURCE_DESCRIPTION_TX)
				#alarmlevel
				rowlist.append(HIGHEST_ALARM_LEVEL)
				#incidentclassification drilldown
				# rowlist.append(INCIDENT_CLASSIFICATION_GROUP)
				rowlist.append(INCIDENT_CLASSIFICATION)
				#rowlist.append(FIRST_ASSIGNMENT_DATETIME)
				# rowlist.append(FIRST_ASSIGNMENT_DATETIME_LIST[0])
				# rowlist.append(FIRST_ASSIGNMENT_DATETIME_LIST[1])
				# rowlist.append(FIRST_ASSIGNMENT_DATETIME_LIST[2])
				# rowlist.append(FIRST_ASSIGNMENT_DATETIME_LIST[3])
				# rowlist.append(FIRST_ASSIGNMENT_DATETIME_LIST[4])
				# rowlist.append(FIRST_ASSIGNMENT_DATETIME_LIST[5])
				#rowlist.append(FIRST_ACTIVATION_DATETIME)
				# rowlist.append(FIRST_ACTIVATION_DATETIME_LIST[0])
				# rowlist.append(FIRST_ACTIVATION_DATETIME_LIST[1])
				# rowlist.append(FIRST_ACTIVATION_DATETIME_LIST[2])
				# rowlist.append(FIRST_ACTIVATION_DATETIME_LIST[3])
				# rowlist.append(FIRST_ACTIVATION_DATETIME_LIST[4])
				# rowlist.append(FIRST_ACTIVATION_DATETIME_LIST[5])
				# #rowlist.append(FIRST_ON_SCENE_DATETIME)
				# rowlist.append(FIRST_ON_SCENE_DATETIME_LIST[0])
				# rowlist.append(FIRST_ON_SCENE_DATETIME_LIST[1])
				# rowlist.append(FIRST_ON_SCENE_DATETIME_LIST[2])
				# rowlist.append(FIRST_ON_SCENE_DATETIME_LIST[3])
				# rowlist.append(FIRST_ON_SCENE_DATETIME_LIST[4])
				# rowlist.append(FIRST_ON_SCENE_DATETIME_LIST[5])
				#rowlist.append(INCIDENT_CLOSE_DATETIME)
				#rowlist.append(VALID_INCIDENT_RSPNS_TIME_INDC) #qualifier (should be 'Y')
				#totalresource drilldown
				rowlist.append(TOTAL_RESOURCE_QTY)
				# rowlist.append(ENGINES_ASSIGNED_QUANTITY)
				# rowlist.append(LADDERS_ASSIGNED_QUANTITY)
				# rowlist.append(OTHER_UNITS_ASSIGNED_QUANTITY)

				#DEPENDENT VARIABLES
				rowlist.append(DISPATCH_RESPONSE_SECONDS_QY)		
				# rowlist.append(INCIDENT_RESPONSE_SECONDS_QY)
				rowlist.append(INCIDENT_TRAVEL_TM_SECONDS_QY)
				rowlist.append(int(INCIDENT_RESOLUTION_SEC.total_seconds()))	
				countrows += 1
				print countrows
				# if countrows == 500000:
				# 	print countrows
				# elif countrows == 1000000:
				# 	print countrows
				# elif countrows == 1400000:
				# 	print countrows
				# else:
				# 	pass

				# with open('cleanfireincidentdataset.csv','a') as csvfile:
				# 	for i in range(0,38):
				# 		csvfile.write(str(rowlist[i]))
				# 		if i == 38:
				# 			csvfile.write('\n')
				# 		else:
				# 			csvfile.write(',')
				totallist.append(rowlist)
				rowlist = []
	# print totallist
#labels = ['Incident Date and Time','INCIDENT_YEAR','INCIDENT_MONTH','INCIDENT_DAY','INCIDENT_WEEKDAY','INCIDENT_QTR_OF_DAY','INCIDENT_TIME','INCIDENT_BOROUGH','Zip Code','Count of Police Precincts','Alarm Source Description','Highest Alarm Level','INCIDENT_CLASSIFICATION_GROUP','Incident Classification','FIRST_ASSIGNMENT_YEAR','FIRST_ASSIGNMENT_MONTH','FIRST_ASSIGNMENT_DAY','FIRST_ASSIGNMENT_WEEKDAY','FIRST_ASSIGNMENT_QTR_OF_DAY','FIRST_ASSIGNMENT_TIME','FIRST_ACTIVATION_YEAR','FIRST_ACTIVATION_MONTH','FIRST_ACTIVATION_DAY','FIRST_ACTIVATION_WEEKDAY','FIRST_ACTIVATION_QTR_OF_DAY','FIRST_ACTIVATION_TIME','FIRST_ON_SCENE_YEAR','FIRST_ON_SCENE_MONTH','FIRST_ON_SCENE_DAY','FIRST_ON_SCENE_WEEKDAY','FIRST_ON_SCENE_QTR_OF_DAY','FIRST_ON_SCENE_TIME','Total Quantity of Resources Dispatched', 'ENGINES_ASSIGNED_QUANTITY','LADDERS_ASSIGNED_QUANTITY', 'OTHER_UNITS_ASSIGNED_QUANTITY','Dispatch Response Time (in seconds)','INCIDENT_RESPONSE_SECONDS_QY','Incident Travel Time (in seconds)','Total Resolution Time (in seconds)']
labels = ['Incident Date and Time','Zip Code','Count of Police Precincts','Alarm Source Description','Highest Alarm Level','Incident Classification','Total Quantity of Resources Dispatched', 'Dispatch Response Time (in seconds)','Incident Travel Time (in seconds)','Total Resolution Time (in seconds)']
df = pd.DataFrame.from_records(totallist,columns = labels)	
df.to_csv('cleanfireincident_final.csv')

*Notes: It’s definitely easier and more elegant to create a working CSV file using Pandas’ DataFrame and .to_csv() functions. The structure is automatically created as a whole.

Creation of unique zipcode-police precinct and count list:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
import pandas as pd
import numpy as np

#create data frame from the clean data set and set integer variables
data = pd.read_csv('cleanfireincident_final.csv', sep = ',', dtype = {'Total Quantity of Resources Dispatched':np.int64, 'Dispatch Response Time (in seconds)': np.int64, 'Incident Travel Time (in seconds)': np.int64, 'Total Resolution Time (in seconds)':np.int64})

#create new dataframe only consisting of zip code and police precinct categories (this is not yet the count)
zcu = data[['Zip Code','Count of Police Precincts']].copy()

#create a group key that will be counted later
countzip =  zcu.groupby(['Zip Code','Count of Police Precincts'])
#count unique instances
counting = countzip.aggregate(len)
#insert zcu and count into another dataframe and rename columns
abc = pd.DataFrame(counting.reset_index())
abc.columns = ['Zip Code','Count of Police Precincts','count']
#export to csv
abc.to_csv('uniquezipcodesandcountsofPP.csv')

 

Matching of Zip Code and insertion of real count of police precincts per zip code:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
import csv
import pandas as pd
import numpy as np
from datetime import date, time, datetime

#initialize lists
listrow = []
totallist = []
rowlist = []
newcsvlist = []
#same process of opening and reading each column by row
with open('uniquezipcodesandcountsofPP.csv', 'rb') as csv_in:
    myreader2 = csv.reader(csv_in, delimiter = ',')
    next(myreader2) #skips column headers    

    for row in myreader2:
        zipcode2 = row[0]
        pp = row[1]
        count = row[2]
        listrow.append(zipcode2)
        listrow.append(pp)
        listrow.append(count)
       #create a list of lists of zip code-precinct-count
        totallist.append(listrow)
        listrow = []


#same process of opening and reading each column by row
with open('cleanfireincident_final.csv', 'rb') as csv_in:
    myreader = csv.reader(csv_in, delimiter = ',')
    next(myreader) #skips column headers

    for row in myreader:
        dateandtime = row[1]
        zipcode= row[2]
        pp = row[3]
        alarmsource = row[4]
        alarmlevel = row[5]
        classif = row[6]
        qty = row[7]
        dispatchtime = row[8]
        traveltime = row[9]
        resoltime = row[10]
        #print classif

        #matching of zipcode and police precincts
        for ziprow in totallist:
            if zipcode == ziprow[0] and pp == ziprow[1]:
                rowlist.append(dateandtime)
                rowlist.append(zipcode)
               #insert count if match
                rowlist.append(ziprow[2])
                rowlist.append(alarmsource)
                rowlist.append(alarmlevel)
                rowlist.append(classif)
                rowlist.append(qty)
                rowlist.append(dispatchtime)
                rowlist.append(traveltime)
                rowlist.append(resoltime)
                newcsvlist.append(rowlist)
                rowlist = []
            else:
                pass
            
labels = ['Incident Date and Time','Zip Code','Count of Police Precincts','Alarm Source Description','Highest Alarm Level','Incident Classification','Total Quantity of Resources Dispatched', 'Dispatch Response Time (in seconds)','Incident Travel Time (in seconds)','Total Resolution Time (in seconds)']
df = pd.DataFrame.from_records(newcsvlist,columns = labels)	
df.to_csv('new.csv')