├── 2014_world_power_consumption_chorlopleth_plot.png ├── 2012_USA_Election_Voting_Age_population_cholopleth_plot.png ├── README.md ├── PandasDataVisualizationExercise_solutions.py ├── Choropleth_Maps_Exercise_solutions.py ├── Seaborn_Exercises_solutions.py ├── Ecommerce_Purchases_solutions.py ├── Logistic_Regression_Project.py ├── Matplotlib_Exercises_solutions.py ├── Numpy+Exercise+.py ├── K_Nearest_Neighbors_Project_code.py ├── SF_Salaries_Exercise_solutions.py ├── Support_Vector_Machines_Project_Solutions_code.py ├── K_Means_Clustering_Project_code.py ├── Python_Exercises_solutions.py ├── Linear_Regression_Project_Solutions.py ├── NLP_Project.py ├── Decision_Trees_and_Random_Forest_Project_source_code.py └── advertising.csv /2014_world_power_consumption_chorlopleth_plot.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ppant/Datascience-MI-Bootcamp-Python/HEAD/2014_world_power_consumption_chorlopleth_plot.png -------------------------------------------------------------------------------- /2012_USA_Election_Voting_Age_population_cholopleth_plot.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ppant/Datascience-MI-Bootcamp-Python/HEAD/2012_USA_Election_Voting_Age_population_cholopleth_plot.png -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Datascience-MI-Bootcamp-Python 2 | Repository for Datascience and Machine learning bootcamp using Python 3 | 4 | * Python crash course exercise solutions 5 | * numPy exercise solutions 6 | * Pandas SF Salary exercise solutions 7 | * Pandas Ecom exercises Purchase solutions 8 | * Matplotlib Exercises solutions 9 | * Choropleth Maps exercises 10 | * Seaborn plotting exercises code 11 | * Output Plots from choroleth maps 2012 USA Election Voting age population 12 | * Output Plots from choroleth maps 2014 World Power Cosumption 13 | * Machine learning Linear Regression modelling project source code 14 | * Machine learning logistic regression project source code 15 | * K Nearest Neighbours project source code 16 | * Decision Tree and Random forests imple source code 17 | -------------------------------------------------------------------------------- /PandasDataVisualizationExercise_solutions.py: -------------------------------------------------------------------------------- 1 | 2 | # coding: utf-8 3 | # Solutions -- code for Pandas built in functions 4 | # # Pandas Data Visualization Exercise 5 | # This is just a quick exercise for you to review the various plots we showed earlier. Use **df3** to replicate the following plots. 6 | # Preparation 7 | import pandas as pd 8 | import matplotlib.pyplot as plt 9 | df3 = pd.read_csv('df3') 10 | # to show the plot in juypter notebook 11 | 12 | get_ipython().magic('matplotlib inline') 13 | df3.info() 14 | # Check top of the head of the data frame 15 | df3.head() 16 | 17 | # Q1: ** Recreate this scatter plot of b vs a. Note the color and size of the points. Also note the figure size. See if you can figure out how to stretch it in a similar fashion. Remeber back to your matplotlib lecture...** 18 | 19 | df3.plot.scatter(x='a',y='b',figsize=(12,3),s=50,c='red') 20 | 21 | # Q1:** Create a histogram of the 'a' column.** 22 | 23 | df3['a'].hist() 24 | 25 | # ** These plots are okay, but they don't look very polished. Use style sheets to set the style to 'ggplot' and redo the histogram from above. Also figure out how to add more bins to it.*** 26 | 27 | plt.style.use('ggplot') 28 | df3['a'].plot.hist(bins=20,alpha=0.5) 29 | # ** Create a boxplot comparing the a and b columns.** 30 | df['a','b'].plot.box() 31 | 32 | # ** Create a kde plot of the 'd' column ** 33 | df3['d'].plot.kde() 34 | # adding line width and line type 35 | df3['d'].plot.kde(lw=5,ls='--') 36 | 37 | 38 | # ** Figure out how to increase the linewidth and make the linestyle dashed. (Note: You would usually not dash a kde plot line)** 39 | 40 | # ** Create an area plot of all the columns for just the rows up to 30. (hint: use .ix).** 41 | df3.ix[0:30].plot.area(alpha=0.4) 42 | f = plt.figure() 43 | df3.ix[0:30].plot.area(alpha=0.4) 44 | # Now to put the legend in such a away so that ot doesn't overlap the actual plot we'll use loc parameter with legend 45 | plt.legend(loc='center left', bbox_to_anchor=(1.0,0.5)) 46 | plt.show() 47 | 48 | # ** Try searching Google for a good stackoverflow link on this topic. If you can't find it on your own - [use this one for a hint.](http://stackoverflow.com/questions/23556153/how-to-put-legend-outside-the-plot-with-pandas)** 49 | 50 | # END -------------------------------------------------------------------------------- /Choropleth_Maps_Exercise_solutions.py: -------------------------------------------------------------------------------- 1 | 2 | # coding: utf-8 3 | # # Choropleth Maps Exercise 4 | # Pradeep K. Pant 5 | # [Full Documentation Reference](https://plot.ly/python/reference/#choropleth) 6 | 7 | # ## Plotly Imports 8 | import plotly.graph_objs as go 9 | from plotly.offline import init_notebook_mode,iplot 10 | init_notebook_mode(connected=True) 11 | # Q1: World Power Consumption 2014 12 | # Basic preparation 13 | # Import pandas and read the csv file: 2014_World_Power_Consumption 14 | import pandas as pd 15 | df = pd.read_csv('2014_World_Power_Consumption') 16 | # Check the head of the DataFrame. 17 | df.head() 18 | # We need to create data and layout variable which contains a dict 19 | data = dict(type='choropleth', 20 | locations = df['Country'], 21 | locationmode = 'country names', 22 | z = df['Power Consumption KWH'], 23 | text = df['Country'], 24 | colorbar = {'title':'Power Consumption KWH'}, 25 | colorscale = 'Viridis', 26 | reversescale = True 27 | ) 28 | 29 | # Lets make a layout 30 | layout = dict(title='2014 World Power Consumption', 31 | geo = dict(showframe=False,projection={'type':'Mercator'})) 32 | # Pass the data and layout and plot using iplot 33 | choromap = go.Figure(data = [data],layout = layout) 34 | iplot(choromap,validate=False) 35 | 36 | # Q2: USA Choropleth 37 | 38 | # Import the 2012_Election_Data csv file using pandas. 39 | usadf = pd.read_csv('2012_Election_Data') 40 | # Check the head of the DataFrame. 41 | usadf.head() 42 | # Now create a plot that displays the Voting-Age Population (VAP) per state. 43 | # First make data dict 44 | data = dict(type='choropleth', 45 | locations=usadf['State Abv'], 46 | locationmode = 'USA-states', 47 | z = usadf['Voting-Age Population (VAP)'], 48 | text = usadf['State'], 49 | colorbar = {'title':'Voting Age Polulation (VAP)'}, 50 | colorscale = 'Viridis', 51 | reversescale = True) 52 | 53 | # Make a nice layout to show all the USA states 54 | layout = dict(title='2012 US Elections: Voting Age Population', 55 | geo = dict(scope='usa', showlakes=True, lakecolor='rgb(85,173.240)')) 56 | # Finally make plot using data and layout 57 | choromap = go.Figure(data = [data],layout = layout) 58 | iplot(choromap,validate=False) 59 | 60 | # END -------------------------------------------------------------------------------- /Seaborn_Exercises_solutions.py: -------------------------------------------------------------------------------- 1 | 2 | # coding: utf-8 3 | 4 | # ___ 5 | # 6 | # 7 | # ___ 8 | # # Seaborn Exercises 9 | # 10 | # Time to practice your new seaborn skills! Try to recreate the plots below (don't worry about color schemes, just the plot itself. 11 | 12 | # ## The Data 13 | # 14 | # We will be working with a famous titanic data set for these exercises. Later on in the Machine Learning section of the course, we will revisit this data, and use it to predict survival rates of passengers. For now, we'll just focus on the visualization of the data with seaborn: 15 | 16 | # In[1]: 17 | 18 | import seaborn as sns 19 | import matplotlib.pyplot as plt 20 | get_ipython().magic('matplotlib inline') 21 | 22 | 23 | # In[2]: 24 | 25 | sns.set_style('whitegrid') 26 | 27 | 28 | # In[3]: 29 | 30 | titanic = sns.load_dataset('titanic') 31 | 32 | 33 | # In[5]: 34 | 35 | titanic.head() 36 | 37 | 38 | # # Exercises 39 | # 40 | # ** Recreate the plots below using the titanic dataframe. There are very few hints since most of the plots can be done with just one or two lines of code and a hint would basically give away the solution. Keep careful attention to the x and y labels for hints.** 41 | # 42 | # ** *Note! In order to not lose the plot image, make sure you don't code in the cell that is directly above the plot, there is an extra cell above that one which won't overwrite that plot!* ** 43 | 44 | # In[42]: 45 | 46 | 47 | 48 | 49 | # In[41]: 50 | 51 | 52 | 53 | 54 | # In[43]: 55 | 56 | # CODE HERE 57 | # REPLICATE EXERCISE PLOT IMAGE BELOW 58 | # BE CAREFUL NOT TO OVERWRITE CELL BELOW 59 | # THAT WOULD REMOVE THE EXERCISE PLOT IMAGE! 60 | 61 | 62 | # In[44]: 63 | 64 | 65 | 66 | 67 | # In[ ]: 68 | 69 | # CODE HERE 70 | # REPLICATE EXERCISE PLOT IMAGE BELOW 71 | # BE CAREFUL NOT TO OVERWRITE CELL BELOW 72 | # THAT WOULD REMOVE THE EXERCISE PLOT IMAGE! 73 | 74 | 75 | # In[45]: 76 | 77 | 78 | 79 | 80 | # In[ ]: 81 | 82 | # CODE HERE 83 | # REPLICATE EXERCISE PLOT IMAGE BELOW 84 | # BE CAREFUL NOT TO OVERWRITE CELL BELOW 85 | # THAT WOULD REMOVE THE EXERCISE PLOT IMAGE! 86 | 87 | 88 | # In[46]: 89 | 90 | 91 | 92 | 93 | # In[ ]: 94 | 95 | # CODE HERE 96 | # REPLICATE EXERCISE PLOT IMAGE BELOW 97 | # BE CAREFUL NOT TO OVERWRITE CELL BELOW 98 | # THAT WOULD REMOVE THE EXERCISE PLOT IMAGE! 99 | 100 | 101 | # In[47]: 102 | 103 | 104 | 105 | 106 | # In[ ]: 107 | 108 | # CODE HERE 109 | # REPLICATE EXERCISE PLOT IMAGE BELOW 110 | # BE CAREFUL NOT TO OVERWRITE CELL BELOW 111 | # THAT WOULD REMOVE THE EXERCISE PLOT IMAGE! 112 | 113 | 114 | # In[48]: 115 | 116 | 117 | 118 | 119 | # In[ ]: 120 | 121 | # CODE HERE 122 | # REPLICATE EXERCISE PLOT IMAGE BELOW 123 | # BE CAREFUL NOT TO OVERWRITE CELL BELOW 124 | # THAT WOULD REMOVE THE EXERCISE PLOT IMAGE! 125 | 126 | 127 | # In[49]: 128 | 129 | 130 | 131 | 132 | # # Great Job! 133 | # 134 | # ### That is it for now! We'll see a lot more of seaborn practice problems in the machine learning section! 135 | -------------------------------------------------------------------------------- /Ecommerce_Purchases_solutions.py: -------------------------------------------------------------------------------- 1 | 2 | # coding: utf-8 3 | 4 | # ___ 5 | # 6 | # 7 | # ___ 8 | # # Ecommerce Purchases Exercise 9 | # 10 | # In this Exercise you will be given some Fake Data about some purchases done through Amazon! Just go ahead and follow the directions and try your best to answer the questions and complete the tasks. Feel free to reference the solutions. Most of the tasks can be solved in different ways. For the most part, the questions get progressively harder. 11 | # 12 | # Please excuse anything that doesn't make "Real-World" sense in the dataframe, all the data is fake and made-up. 13 | # 14 | # Also note that all of these questions can be answered with one line of code. 15 | # ____ 16 | # ** Import pandas and read in the Ecommerce Purchases csv file and set it to a DataFrame called ecom. ** 17 | 18 | # In[8]: 19 | 20 | import pandas as pd 21 | 22 | 23 | # In[9]: 24 | 25 | ecom = pd.read_csv("Ecommerce Purchases") 26 | 27 | 28 | # **Check the head of the DataFrame.** 29 | 30 | # In[11]: 31 | 32 | ecom.head() 33 | 34 | 35 | # In[ ]: 36 | 37 | 38 | 39 | 40 | # ** How many rows and columns are there? ** 41 | 42 | # In[12]: 43 | 44 | ecom.info() 45 | 46 | 47 | # In[ ]: 48 | 49 | 50 | 51 | 52 | # ** What is the average Purchase Price? ** 53 | 54 | # In[14]: 55 | 56 | ecom['Purchase Price'].mean() 57 | 58 | 59 | # In[ ]: 60 | 61 | 62 | 63 | 64 | # ** What were the highest and lowest purchase prices? ** 65 | 66 | # In[ ]: 67 | 68 | ecom[] 69 | 70 | 71 | # In[ ]: 72 | 73 | 74 | 75 | 76 | # In[93]: 77 | 78 | 79 | 80 | 81 | # ** How many people have English 'en' as their Language of choice on the website? ** 82 | 83 | # In[94]: 84 | 85 | 86 | 87 | 88 | # ** How many people have the job title of "Lawyer" ? ** 89 | # 90 | 91 | # In[95]: 92 | 93 | 94 | 95 | 96 | # ** How many people made the purchase during the AM and how many people made the purchase during PM ? ** 97 | # 98 | # **(Hint: Check out [value_counts()](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.value_counts.html) ) ** 99 | 100 | # In[96]: 101 | 102 | 103 | 104 | 105 | # ** What are the 5 most common Job Titles? ** 106 | 107 | # In[97]: 108 | 109 | 110 | 111 | 112 | # ** Someone made a purchase that came from Lot: "90 WT" , what was the Purchase Price for this transaction? ** 113 | 114 | # In[99]: 115 | 116 | 117 | 118 | 119 | # ** What is the email of the person with the following Credit Card Number: 4926535242672853 ** 120 | 121 | # In[100]: 122 | 123 | 124 | 125 | 126 | # ** How many people have American Express as their Credit Card Provider *and* made a purchase above $95 ?** 127 | 128 | # In[101]: 129 | 130 | 131 | 132 | 133 | # ** Hard: How many people have a credit card that expires in 2025? ** 134 | 135 | # In[102]: 136 | 137 | 138 | 139 | 140 | # ** Hard: What are the top 5 most popular email providers/hosts (e.g. gmail.com, yahoo.com, etc...) ** 141 | 142 | # In[56]: 143 | 144 | 145 | 146 | 147 | # # Great Job! 148 | -------------------------------------------------------------------------------- /Logistic_Regression_Project.py: -------------------------------------------------------------------------------- 1 | 2 | # coding: utf-8 3 | 4 | # Author: Pradeep K. Pant 5 | # Logistic Regression Project 6 | 7 | # In this project we will be working with a fake advertising data set, indicating whether or not a particular internet user clicked on an Advertisement. We will try to create a model that will predict whether or not they will click on an ad based off the features of that user. 8 | # 9 | # This data set contains the following features: 10 | # 11 | # * 'Daily Time Spent on Site': consumer time on site in minutes 12 | # * 'Age': cutomer age in years 13 | # * 'Area Income': Avg. Income of geographical area of consumer 14 | # * 'Daily Internet Usage': Avg. minutes a day consumer is on the internet 15 | # * 'Ad Topic Line': Headline of the advertisement 16 | # * 'City': City of consumer 17 | # * 'Male': Whether or not consumer was male 18 | # * 'Country': Country of consumer 19 | # * 'Timestamp': Time at which consumer clicked on Ad or closed window 20 | # * 'Clicked on Ad': 0 or 1 indicated clicking on Ad 21 | # 22 | # ## Import Libraries. pandas, numpy, matplotlib, serborn etc 23 | import pandas as pd 24 | import numpy as np 25 | import matplotlib.pyplot as plt 26 | import seaborn as sns 27 | # This is for inline plotting in juypyer notebook 28 | get_ipython().magic('matplotlib inline') 29 | 30 | # Get the Data 31 | # Read in the advertising.csv file and set it to a data frame called ad_data. 32 | # Assume the file is in same path 33 | ad_data = pd.read_csv('advertising.csv') 34 | 35 | # Check the head of ad_data 36 | ad_data.head() 37 | 38 | # Use info and describe() on ad_data 39 | # This will give onfo about how distubuted data is etc also the total no opf rows and columns 40 | ad_data.describe 41 | ad_info() 42 | 43 | # Exploratory Data Analysis 44 | 45 | # Let's use seaborn plotting system to explore the data 46 | # Create a histogram of the Age 47 | ad_data['Age'].plot.hist(bins=30) 48 | 49 | # Create a jointplot showing Area Income versus Age. 50 | sns.jointplot(x='Age', y='Area Income', data=ad_data) 51 | 52 | # Create a jointplot showing the kde distributions of Daily Time spent on site vs. Age. 53 | sns.jointplot(x='Age', y='Daily Time Spent on Site', data=ad_data, kind='kde',color='red') 54 | 55 | # Create a jointplot of 'Daily Time Spent on Site' vs. 'Daily Internet Usage' 56 | sns.jointplot(x='Daily Time Spent on Site',y='Daily Internet Usage',data=ad_data,color='green') 57 | 58 | # Finally, create a pairplot with the hue defined by the 'Clicked on Ad' column feature. 59 | sns.pairplot(ad_data,hue='Clicked on Ad') 60 | 61 | 62 | # Logistic Regression 63 | # Now it's time to do a train test split, and train our model 64 | # You'll have the freedom here to choose columns that you want to train on! later we can take other columns too 65 | 66 | # Split the data into training set and testing set using train_test_split 67 | from sklearn.cross_validation import train_test_split 68 | X = ad_data[['Daily Time Spent on Site','Age','Area Income','Daily Internet Usage','Male']] 69 | y = ad_data['Clicked on Ad'] 70 | # Split the data to train and test set 30-70 ratio, random state can be chosen any number 71 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101) 72 | 73 | # Train and fit a logistic regression model on the training set.** 74 | from sklearn.linear_model import LogisticRegression 75 | # Instantiate LogisticRegression 76 | logmodel = LogisticRegression() 77 | logmodel.fit(X_train,y_train) 78 | 79 | # Predictions and Evaluations 80 | # Now predict values for the testing data. 81 | predictions = logmodel.predict(X_test) 82 | 83 | # Create a classification report and confusion matrix for the model. 84 | from sklearn.metrics import classification_report, confusion_matrix 85 | print (classification_report(y_test,predictions)) 86 | print (confusion_matrix(y_test,predictions)) 87 | # End -------------------------------------------------------------------------------- /Matplotlib_Exercises_solutions.py: -------------------------------------------------------------------------------- 1 | 2 | # coding: utf-8 3 | 4 | # ___ 5 | # 6 | # 7 | # ___ 8 | # # Matplotlib Exercises 9 | # 10 | # Welcome to the exercises for reviewing matplotlib! Take your time with these, Matplotlib can be tricky to understand at first. These are relatively simple plots, but they can be hard if this is your first time with matplotlib, feel free to reference the solutions as you go along. 11 | # 12 | # Also don't worry if you find the matplotlib syntax frustrating, we actually won't be using it that often throughout the course, we will switch to using seaborn and pandas built-in visualization capabilities. But, those are built-off of matplotlib, which is why it is still important to get exposure to it! 13 | # 14 | # ** * NOTE: ALL THE COMMANDS FOR PLOTTING A FIGURE SHOULD ALL GO IN THE SAME CELL. SEPARATING THEM OUT INTO MULTIPLE CELLS MAY CAUSE NOTHING TO SHOW UP. * ** 15 | # 16 | # # Exercises 17 | # 18 | # Follow the instructions to recreate the plots using this data: 19 | # 20 | # ## Data 21 | 22 | # In[4]: 23 | 24 | import numpy as np 25 | x = np.arange(0,100) 26 | y = x*2 27 | z = x**2 28 | 29 | 30 | # ** Import matplotlib.pyplot as plt and set %matplotlib inline if you are using the jupyter notebook. What command do you use if you aren't using the jupyter notebook?** 31 | 32 | # In[23]: 33 | 34 | import matplotlib.pyplot as plt 35 | import matplotlib.pyplot as plt 36 | import matplotlib.patches as patches 37 | get_ipython().magic('matplotlib inline') 38 | 39 | 40 | # ## Exercise 1 41 | # 42 | # ** Follow along with these steps: ** 43 | # * ** Create a figure object called fig using plt.figure() ** 44 | # * ** Use add_axes to add an axis to the figure canvas at [0,0,1,1]. Call this new axis ax. ** 45 | # * ** Plot (x,y) on that axes and set the labels and titles to match the plot below:** 46 | 47 | # In[6]: 48 | 49 | fig = plt.figure() 50 | ax = fig.add_axes([0,0,1,1]) 51 | ax.plot(x,y,'b') 52 | ax.set_xlabel('x') 53 | ax.set_ylabel('y') 54 | ax.set_title('title') 55 | 56 | 57 | # ## Exercise 2 58 | # ** Create a figure object and put two axes on it, ax1 and ax2. Located at [0,0,1,1] and [0.2,0.5,.2,.2] respectively.** 59 | 60 | # In[12]: 61 | 62 | fig = plt.figure() 63 | ax1 = fig.add_axes([0,0,1,1]) 64 | ax2 = fig.add_axes([0.2,0.5,.2,.2]) 65 | 66 | 67 | 68 | 69 | # In[ ]: 70 | 71 | 72 | 73 | 74 | # ** Now plot (x,y) on both axes. And call your figure object to show it.** 75 | 76 | # In[13]: 77 | 78 | ax1.plot(x,y,'r') 79 | ax2.plot(x,y,'r') 80 | 81 | 82 | # In[14]: 83 | 84 | fig.show() 85 | 86 | 87 | # ## Exercise 3 88 | # 89 | # ** Create the plot below by adding two axes to a figure object at [0,0,1,1] and [0.2,0.5,.4,.4]** 90 | 91 | # In[16]: 92 | 93 | fig = plt.figure() 94 | ax1 = fig.add_axes( [0,0,1,1]) 95 | ax2 = fig.add_axes([0.2,0.5,.4,.4]) 96 | 97 | 98 | # In[ ]: 99 | 100 | 101 | 102 | 103 | # ** Now use x,y, and z arrays to recreate the plot below. Notice the xlimits and y limits on the inserted plot:** 104 | 105 | # In[29]: 106 | 107 | ax1.plot(x,z,'b') 108 | ax1.set_xlabel('x') 109 | ax1.set_ylabel('z') 110 | ax1.set_title('zoom') 111 | ax2.plot(x,y,'b') 112 | ax2.set_xlabel('x') 113 | ax2.set_ylabel('y') 114 | 115 | 116 | # In[32]: 117 | 118 | ax1.show() 119 | 120 | 121 | # ## Exercise 4 122 | # 123 | # ** Use plt.subplots(nrows=1, ncols=2) to create the plot below.** 124 | 125 | # In[48]: 126 | 127 | 128 | 129 | 130 | # ** Now plot (x,y) and (x,z) on the axes. Play around with the linewidth and style** 131 | 132 | # In[51]: 133 | 134 | 135 | 136 | 137 | # ** See if you can resize the plot by adding the figsize() argument in plt.subplots() are copying and pasting your previous code.** 138 | 139 | # In[32]: 140 | 141 | 142 | 143 | 144 | # # Great Job! 145 | -------------------------------------------------------------------------------- /Numpy+Exercise+.py: -------------------------------------------------------------------------------- 1 | 2 | # coding: utf-8 3 | # Solutions: Code for numpy library exercises 4 | # Pradeep K. Pant @2017 5 | # ___ 6 | # 7 | # 8 | # ___ 9 | 10 | # # NumPy Exercises 11 | # 12 | # Now that we've learned about NumPy let's test your knowledge. We'll start off with a few simple tasks, and then you'll be asked some more complicated questions. 13 | 14 | # #### Import NumPy as np 15 | 16 | # In[2]: 17 | 18 | import numpy as np 19 | 20 | 21 | # #### Create an array of 10 zeros 22 | 23 | # In[3]: 24 | 25 | np.zeros(10) 26 | 27 | 28 | # In[ ]: 29 | 30 | 31 | 32 | 33 | # In[2]: 34 | 35 | 36 | 37 | 38 | # In[ ]: 39 | 40 | 41 | 42 | 43 | # In[ ]: 44 | 45 | 46 | 47 | 48 | # #### Create an array of 10 ones 49 | 50 | # In[4]: 51 | 52 | np.ones(10) 53 | 54 | 55 | # #### Create an array of 10 fives 56 | 57 | # In[8]: 58 | 59 | newarr = np.ones(10) 60 | 61 | 62 | # In[9]: 63 | 64 | newarr*5 65 | 66 | 67 | # In[4]: 68 | 69 | 70 | 71 | 72 | # #### Create an array of the integers from 10 to 50 73 | 74 | # In[14]: 75 | 76 | np.arange(10,51) 77 | 78 | 79 | # In[5]: 80 | 81 | 82 | 83 | 84 | # #### Create an array of all the even integers from 10 to 50 85 | 86 | # In[16]: 87 | 88 | np.arange(10,52,2) 89 | 90 | 91 | # In[6]: 92 | 93 | 94 | 95 | 96 | # #### Create a 3x3 matrix with values ranging from 0 to 8 97 | 98 | # In[71]: 99 | 100 | np.arange(9).reshape(3,3) 101 | 102 | 103 | # In[7]: 104 | 105 | 106 | 107 | 108 | # #### Create a 3x3 identity matrix 109 | 110 | # In[18]: 111 | 112 | np.eye(3) 113 | 114 | 115 | # #### Use NumPy to generate a random number between 0 and 1 116 | 117 | # In[31]: 118 | 119 | np.random.rand(1) 120 | 121 | 122 | # In[15]: 123 | 124 | 125 | 126 | 127 | # #### Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution 128 | 129 | # In[32]: 130 | 131 | np.random.randn(25) 132 | 133 | 134 | # In[33]: 135 | 136 | 137 | 138 | 139 | # #### Create the following matrix: 140 | 141 | # In[72]: 142 | 143 | np.linspace(0.01,1,100).reshape(10,10) 144 | 145 | 146 | # In[ ]: 147 | 148 | 149 | 150 | 151 | # 152 | 153 | # In[35]: 154 | 155 | 156 | 157 | 158 | # #### Create an array of 20 linearly spaced points between 0 and 1: 159 | 160 | # In[40]: 161 | 162 | np.linspace(0,1,20) 163 | 164 | 165 | # In[36]: 166 | 167 | 168 | 169 | 170 | # ## Numpy Indexing and Selection 171 | # 172 | # Now you will be given a few matrices, and be asked to replicate the resulting matrix outputs: 173 | 174 | # In[41]: 175 | 176 | mat = np.arange(1,26).reshape(5,5) 177 | mat 178 | 179 | 180 | # In[73]: 181 | 182 | mat[2:,1:] 183 | 184 | 185 | # In[ ]: 186 | 187 | 188 | 189 | 190 | # In[ ]: 191 | 192 | 193 | 194 | 195 | # In[ ]: 196 | 197 | 198 | 199 | 200 | # In[ ]: 201 | 202 | 203 | 204 | 205 | # In[29]: 206 | 207 | # WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW 208 | # BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T 209 | # BE ABLE TO SEE THE OUTPUT ANY MORE 210 | 211 | 212 | # In[75]: 213 | 214 | mat[3,4] 215 | 216 | 217 | # In[ ]: 218 | 219 | 220 | 221 | 222 | # In[ ]: 223 | 224 | 225 | 226 | 227 | # In[30]: 228 | 229 | 230 | 231 | 232 | # In[ ]: 233 | 234 | 235 | 236 | 237 | # In[77]: 238 | 239 | mat[:3,1:2] 240 | 241 | 242 | # In[79]: 243 | 244 | mat[4,:] 245 | 246 | 247 | # In[46]: 248 | 249 | 250 | 251 | 252 | # In[32]: 253 | 254 | # WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW 255 | # BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T 256 | # BE ABLE TO SEE THE OUTPUT ANY MORE 257 | 258 | 259 | # In[80]: 260 | 261 | mat[3:5,:] 262 | 263 | 264 | # ### Now do the following 265 | 266 | # #### Get the sum of all the values in mat 267 | 268 | # In[81]: 269 | 270 | mat 271 | 272 | 273 | # In[82]: 274 | 275 | np.sum(mat) 276 | 277 | 278 | # #### Get the standard deviation of the values in mat 279 | 280 | # In[83]: 281 | 282 | np.std(mat) 283 | 284 | 285 | # #### Get the sum of all the columns in mat 286 | 287 | # In[84]: 288 | 289 | mat 290 | 291 | 292 | # In[85]: 293 | 294 | mat.sum(axis=0) 295 | 296 | 297 | # # Great Job! 298 | -------------------------------------------------------------------------------- /K_Nearest_Neighbors_Project_code.py: -------------------------------------------------------------------------------- 1 | 2 | # coding: utf-8 3 | 4 | # ___ 5 | # 6 | #Auther: Pradeep K. Pant 7 | # ___ 8 | 9 | # # K Nearest Neighbors Project 10 | # 11 | # Import Libraries 12 | # Import pandas,seaborn, and the usual libraries. 13 | 14 | import pandas as pd 15 | import numpy as np 16 | import matplotlib.pyplot as plt 17 | import seaborn as sns 18 | # This step is needed to show plt/graph in jupyter notebook 19 | get_ipython().magic('matplotlib inline') 20 | 21 | # Get the Data 22 | # Read the 'KNN_Project_Data csv file into a dataframe 23 | 24 | projectData = pd.read_csv('KNN_Project_Data') 25 | 26 | # Check the head of the dataframe. 27 | 28 | projectData.head() 29 | 30 | # Since this data is artificial, we'll just do a large pairplot with seaborn. 31 | # Use seaborn on the dataframe to create a pairplot with the hue indicated by the TARGET CLASS column. 32 | 33 | sns.pairplot(projectData,hue='TARGET CLASS',palette='coolwarm') 34 | 35 | # Standardize the Variables 36 | # Time to standardize the variables. 37 | 38 | # Import StandardScaler from Scikit learn. 39 | 40 | from sklearn.preprocessing import StandardScaler 41 | 42 | 43 | # Now Create a StandardScaler() object called scaler 44 | 45 | scalar = StandardScaler() 46 | 47 | # Fit scaler to the features. 48 | 49 | scalar.fit(projectData.drop('TARGET CLASS',axis=1)) 50 | 51 | # Use the .transform() method to transform the features to a scaled version. 52 | 53 | scaledFeature = scalar.transform(projectData.drop('TARGET CLASS',axis=1)) 54 | 55 | # Convert the scaled features to a dataframe and check the head of this dataframe to make sure the scaling worked. 56 | 57 | projectData_features = pd.DataFrame(scaledFeature,columns=projectData.columns[:-1]) 58 | projectData_features.head() 59 | 60 | # Train Test Split 61 | # 62 | # Use train_test_split to split your data into a training set and a testing set. 63 | 64 | from sklearn.cross_validation import train_test_split 65 | 66 | # Prepare X and y variables 67 | X = projectData_features 68 | y = projectData['TARGET CLASS'] 69 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=101) 70 | 71 | 72 | # Using KNN 73 | # Import KNeighborsClassifier from scikit learn. 74 | 75 | from sklearn.neighbors import KNeighborsClassifier 76 | 77 | # Create a KNN model instance with n_neighbors=1 78 | 79 | knn = KNeighborsClassifier(n_neighbors=1) 80 | 81 | 82 | # Fit this KNN model to the training data. 83 | 84 | knn.fit(X_train,y_train) 85 | 86 | # Predictions and Evaluations 87 | # Let's evaluate our KNN model! 88 | 89 | # Use the predict method to predict values using your KNN model and X_test. 90 | 91 | pred = knn.predict(X_test) 92 | 93 | # Create a confusion matrix and classification report. 94 | 95 | from sklearn.metrics import classification_report, confusion_matrix 96 | 97 | # Print confusion matrix and classicfication report 98 | print (confusion_matrix(y_test,pred)) 99 | print (classification_report(y_test,pred)) 100 | 101 | # # Choosing a K Value 102 | # Let's go ahead and use the elbow method to pick a good K Value! 103 | # 104 | # Create a for loop that trains various KNN models with different k values, then keep track of the error_rate for each of these models with a list. Refer to the lecture if you are confused on this step. 105 | 106 | error_rate = [] 107 | for i in range (1,40): 108 | knn = KNeighborsClassifier(n_neighbors=i) 109 | knn.fit(X_train,y_train) 110 | pred_i = knn.predict(X_test) 111 | error_rate.append(np.mean(pred_i != y_test)) 112 | 113 | # Now create the following plot using the information from your for loop. 114 | 115 | plt.figure(figsize=(10,6)) 116 | plt.plot(range(1,40),error_rate,color='blue',linestyle='--',marker='o',markerfacecolor='red',markersize=10) 117 | plt.title('Error Rate vs K') 118 | plt.xlabel('K') 119 | plt.ylabel('Error rate') 120 | 121 | 122 | # Retrain with new K Value 123 | # Retrain your model with the best K value (up to you to decide what you want) and re-do the classification report and the confusion matrix. 124 | 125 | # Afterlooking into plot we can check with the K = 30 126 | knn = KNeighborsClassifier(n_neighbors=30) 127 | knn.fit(X_train,y_train) 128 | pred = knn.predict(X_test) 129 | print (confusion_matrix(y_test,pred)) 130 | print ('\n') 131 | print (classification_report(y_test,pred)) 132 | 133 | # END 134 | -------------------------------------------------------------------------------- /SF_Salaries_Exercise_solutions.py: -------------------------------------------------------------------------------- 1 | 2 | # coding: utf-8 3 | # Solutions: Code for SF Salary exercises using nymPy and Pandas 4 | # Pradeep K. Pant @2017 5 | # ___ 6 | # 7 | # 8 | # ___ 9 | 10 | # # SF Salaries Exercise 11 | # 12 | # Welcome to a quick exercise for you to practice your pandas skills! We will be using the [SF Salaries Dataset](https://www.kaggle.com/kaggle/sf-salaries) from Kaggle! Just follow along and complete the tasks outlined in bold below. The tasks will get harder and harder as you go along. 13 | 14 | # ** Import pandas as pd.** 15 | 16 | # In[2]: 17 | 18 | 19 | import pandas as pd 20 | 21 | 22 | # ** Read Salaries.csv as a dataframe called sal.** 23 | 24 | # In[3]: 25 | 26 | sal = pd.read_csv("Salaries.csv") 27 | 28 | 29 | # ** Check the head of the DataFrame. ** 30 | 31 | # In[5]: 32 | 33 | sal.head() 34 | 35 | 36 | # ** Use the .info() method to find out how many entries there are.** 37 | 38 | # In[6]: 39 | 40 | sal.info 41 | 42 | 43 | # **What is the average BasePay ?** 44 | 45 | # In[11]: 46 | 47 | sal['BasePay'].mean() 48 | 49 | 50 | # In[ ]: 51 | 52 | 53 | 54 | 55 | # ** What is the highest amount of OvertimePay in the dataset ? ** 56 | 57 | # In[12]: 58 | 59 | sal['OvertimePay'].max() 60 | 61 | 62 | # In[11]: 63 | 64 | 65 | 66 | 67 | # In[ ]: 68 | 69 | 70 | 71 | 72 | # In[ ]: 73 | 74 | 75 | 76 | 77 | # ** What is the job title of JOSEPH DRISCOLL ? Note: Use all caps, otherwise you may get an answer that doesn't match up (there is also a lowercase Joseph Driscoll). ** 78 | 79 | # In[13]: 80 | 81 | sal[sal['EmployeeName']== 'JOSEPH DRISCOLL'] 82 | 83 | 84 | # In[ ]: 85 | 86 | 87 | 88 | 89 | # ** How much does JOSEPH DRISCOLL make (including benefits)? ** 90 | 91 | # In[16]: 92 | 93 | sal[sal['EmployeeName']== 'JOSEPH DRISCOLL']['TotalPayBenefits'] 94 | 95 | 96 | # In[ ]: 97 | 98 | 99 | 100 | 101 | # ** What is the name of highest paid person (including benefits)?** 102 | 103 | # In[27]: 104 | 105 | sal[sal['TotalPayBenefits'] == sal['TotalPayBenefits'].max()]['EmployeeName'] 106 | 107 | 108 | # In[ ]: 109 | 110 | 111 | 112 | 113 | # In[ ]: 114 | 115 | 116 | 117 | 118 | # In[ ]: 119 | 120 | 121 | 122 | 123 | # ** What is the name of lowest paid person (including benefits)? Do you notice something strange about how much he or she is paid?** 124 | 125 | # In[ ]: 126 | 127 | 128 | 129 | 130 | # In[28]: 131 | 132 | sal[sal['TotalPayBenefits'] == sal['TotalPayBenefits'].min()]['EmployeeName'] 133 | 134 | 135 | # In[31]: 136 | 137 | sal.iloc[sal['TotalPayBenefits'].argmin()]['EmployeeName'] 138 | 139 | 140 | # In[15]: 141 | 142 | 143 | 144 | 145 | # In[ ]: 146 | 147 | 148 | 149 | 150 | # In[ ]: 151 | 152 | 153 | 154 | 155 | # ** What was the average (mean) BasePay of all employees per year? (2011-2014) ? ** 156 | 157 | # In[34]: 158 | 159 | sal.groupby('Year').mean()['BasePay'] 160 | 161 | 162 | # In[ ]: 163 | 164 | 165 | 166 | 167 | # In[ ]: 168 | 169 | 170 | 171 | 172 | # In[ ]: 173 | 174 | 175 | 176 | 177 | # In[ ]: 178 | 179 | 180 | 181 | 182 | # In[ ]: 183 | 184 | 185 | 186 | 187 | # In[ ]: 188 | 189 | 190 | 191 | 192 | # In[ ]: 193 | 194 | 195 | 196 | 197 | # In[ ]: 198 | 199 | 200 | 201 | 202 | # In[ ]: 203 | 204 | 205 | 206 | 207 | # ** How many unique job titles are there? ** 208 | 209 | # In[54]: 210 | 211 | sal['JobTitle'].nunique() 212 | 213 | 214 | # ** What are the top 5 most common jobs? ** 215 | 216 | # In[53]: 217 | 218 | sal['JobTitle'].value_counts().head(5) 219 | 220 | 221 | # ** How many Job Titles were represented by only one person in 2013? (e.g. Job Titles with only one occurence in 2013?) ** 222 | 223 | # In[52]: 224 | 225 | sum(sal[sal['Year']==2013]['JobTitle'].value_counts() == 1) 226 | 227 | 228 | # ** How many people have the word Chief in their job title? (This is pretty tricky) ** 229 | 230 | # In[20]: 231 | 232 | def checkStringForWord (title): 233 | if 'chief' in title.lower().split(): 234 | return True 235 | else: 236 | return False 237 | 238 | 239 | # In[21]: 240 | 241 | sum(sal['JobTitle'].apply(lambda x:checkStringForWord(x))) 242 | 243 | 244 | # ** Bonus: Is there a correlation between length of the Job Title string and Salary? ** 245 | 246 | # In[59]: 247 | 248 | sal['title_len'] = sal['JobTitle'].apply(len) 249 | 250 | 251 | # In[60]: 252 | 253 | sal[['JobTitle','title_len']].corr() 254 | 255 | 256 | # In[23]: 257 | 258 | 259 | 260 | 261 | # # Great Job! 262 | -------------------------------------------------------------------------------- /Support_Vector_Machines_Project_Solutions_code.py: -------------------------------------------------------------------------------- 1 | 2 | # coding: utf-8 3 | 4 | # Pradeep K. Pant 5 | # Support Vector Machines Project 6 | 7 | # ## The Data 8 | # For this series of lectures, we will be using the famous [Iris flower data set](http://en.wikipedia.org/wiki/Iris_flower_data_set). 9 | # 10 | # The Iris flower data set or Fisher's Iris data set is a multivariate data set introduced by Sir Ronald Fisher in the 1936 as an example of discriminant analysis. 11 | # 12 | # The data set consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor), so 150 total samples. Four features were measured from each sample: the length and the width of the sepals and petals, in centimeters. 13 | 14 | # Here's a picture of the three different Iris types: 15 | 16 | # The Iris Setosa 17 | from IPython.display import Image 18 | url = 'http://upload.wikimedia.org/wikipedia/commons/5/56/Kosaciec_szczecinkowaty_Iris_setosa.jpg' 19 | Image(url,width=300, height=300) 20 | 21 | # The Iris Versicolor 22 | from IPython.display import Image 23 | url = 'http://upload.wikimedia.org/wikipedia/commons/4/41/Iris_versicolor_3.jpg' 24 | Image(url,width=300, height=300) 25 | 26 | # The Iris Virginica 27 | from IPython.display import Image 28 | url = 'http://upload.wikimedia.org/wikipedia/commons/9/9f/Iris_virginica.jpg' 29 | Image(url,width=300, height=300) 30 | 31 | 32 | # The iris dataset contains measurements for 150 iris flowers from three different species. 33 | # 34 | # The three classes in the Iris dataset: 35 | # 36 | # Iris-setosa (n=50) 37 | # Iris-versicolor (n=50) 38 | # Iris-virginica (n=50) 39 | # 40 | # The four features of the Iris dataset: 41 | # 42 | # sepal length in cm 43 | # sepal width in cm 44 | # petal length in cm 45 | # petal width in cm 46 | # 47 | # ## Get the data 48 | 49 | import seaborn as sns 50 | iris = sns.load_dataset('iris') 51 | 52 | # Let's visualize the data and get you started! 53 | # Exploratory Data Analysis 54 | 55 | # Import libraries needed 56 | 57 | import pandas as pd 58 | import numpy as np 59 | get_ipython().magic('matplotlib inline') 60 | 61 | # Create a pairplot of the data set. Which flower species seems to be the most separable? 62 | 63 | sns.pairplot(iris,hue='species',palette="Dark2") 64 | 65 | # Create a kde plot of sepal_length versus sepal width for setosa species of flower. 66 | 67 | setosa = iris[iris['species'] == 'setosa'] 68 | sns.kdeplot(setosa['sepal_width'],setosa['sepal_length'],cmap='plasma',shade=True,shade_lowest=False) 69 | 70 | 71 | # Train Test Split 72 | # Split your data into a training set and a testing set. 73 | 74 | from sklearn.cross_validation import train_test_split 75 | # Make a X and Y parameters 76 | X = iris.drop('species', axis=1) 77 | y = iris['species'] 78 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3) 79 | 80 | 81 | # Train a Model 82 | # 83 | # Now its time to train a Support Vector Machine Classifier. 84 | # 85 | # Call the SVC() model from sklearn and fit the model to the training data. 86 | 87 | from sklearn.svm import SVC 88 | 89 | # Instantiate SVC object 90 | svc_model = SVC() 91 | svc_model.fit(X_train,y_train) 92 | 93 | # Model Evaluation 94 | # Now get predictions from the model and create a confusion matrix and a classification report. 95 | 96 | predictions = svc_model.predict(X_test) 97 | from sklearn.metrics import classification_report, confusion_matrix 98 | 99 | print (confusion_matrix(y_test,predictions)) 100 | 101 | print (classification_report(y_test,predictions)) 102 | 103 | 104 | # Looking into confusion matrix and classification reports it seems that model was pretty good! Let's see if we can tune the parameters to try to get even better (unlikely, and you probably would be satisfied with these results in real like because the data set is quite small, but I just want you to practice using GridSearch. 105 | 106 | # Gridsearch Practice 107 | # 108 | # Import GridsearchCV from SciKit Learn. 109 | 110 | from sklearn.grid_search import GridSearchCV 111 | 112 | 113 | # Create a dictionary called param_grid and fill out some parameters for C and gamma. 114 | 115 | param_grid = {'C':[0.1,1,10,100],'gamma':[1,0.1,0,0.01,0.001]} 116 | 117 | # Create a GridSearchCV object and fit it to the training data. 118 | 119 | grid = GridSearchCV(SVC(),param_grid,verbose=2) 120 | grid.fit(X_train,y_train) 121 | 122 | 123 | # Now take that grid model and create some predictions using the test set and create classification reports and confusion matrices for them. Were you able to improve? 124 | 125 | grid_predictions = grid.predict(X_test) 126 | 127 | print (confusion_matrix(y_test,grid_predictions)) 128 | 129 | print (classification_report(y_test,grid_predictions)) 130 | 131 | # We can see now that there is a slight imporvements in results using grid search method though data set is very small. For larger dataset one might see significant change in results. -------------------------------------------------------------------------------- /K_Means_Clustering_Project_code.py: -------------------------------------------------------------------------------- 1 | 2 | # coding: utf-8 3 | # Author: Pradeep K. Pant 4 | 5 | # K Means Clustering Project 6 | # 7 | # For this project we will attempt to use KMeans Clustering to cluster Universities into to two groups, Private and Public. 8 | # 9 | # It is very important to note, we actually have the labels for this data set, but we will NOT use them for the KMeans clustering algorithm, since that is an unsupervised learning algorithm. 10 | # 11 | # When using the Kmeans algorithm under normal circumstances, it is because you don't have labels. In this case we will use the labels to try to get an idea of how well the algorithm performed, but you won't usually do this for Kmeans, so the classification report and confusion matrix at the end of this project, don't truly make sense in a real world setting!. 12 | 13 | # The Data 14 | # 15 | # We will use a data frame with 777 observations on the following 18 variables. 16 | # * Private A factor with levels No and Yes indicating private or public university 17 | # * Apps Number of applications received 18 | # * Accept Number of applications accepted 19 | # * Enroll Number of new students enrolled 20 | # * Top10perc Pct. new students from top 10% of H.S. class 21 | # * Top25perc Pct. new students from top 25% of H.S. class 22 | # * F.Undergrad Number of fulltime undergraduates 23 | # * P.Undergrad Number of parttime undergraduates 24 | # * Outstate Out-of-state tuition 25 | # * Room.Board Room and board costs 26 | # * Books Estimated book costs 27 | # * Personal Estimated personal spending 28 | # * PhD Pct. of faculty with Ph.D.’s 29 | # * Terminal Pct. of faculty with terminal degree 30 | # * S.F.Ratio Student/faculty ratio 31 | # * perc.alumni Pct. alumni who donate 32 | # * Expend Instructional expenditure per student 33 | # * Grad.Rate Graduation rate 34 | 35 | # Import Libraries 36 | import pandas as pd 37 | import numpy as np 38 | import matplotlib.pyplot as plt 39 | import seaborn as sns 40 | get_ipython().magic('matplotlib inline') 41 | 42 | # Get the Data 43 | 44 | # Read in the College_Data file using read_csv. We'll set the first column as the index. 45 | 46 | df = pd.read_csv("College_Data",index_col=0) 47 | 48 | 49 | # Check the head of the data 50 | 51 | df.head() 52 | 53 | 54 | # Check the info() and describe() methods on the data. 55 | 56 | df.info() 57 | 58 | 59 | df.describe() 60 | 61 | 62 | # It's time to create some data visualizations! 63 | # 64 | # Create a scatterplot of Grad.Rate versus Room.Board where the points are colored by the Private column. 65 | 66 | sns.lmplot(x='Room.Board',y='Grad.Rate',data=df,hue='Private',fit_reg=False,palette='coolwarm',size=6,aspect=1) 67 | 68 | 69 | # Create a scatterplot of F.Undergrad versus Outstate where the points are colored by the Private column. 70 | 71 | sns.lmplot(x='Outstate',y='F.Undergrad',data=df,hue='Private',fit_reg=False,size=6,aspect=1) 72 | 73 | # Create a stacked histogram showing Out of State Tuition based on the Private column. Try doing this using [sns.FacetGrid](https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.FacetGrid.html). If that is too tricky, see if you can do it just by using two instances of pandas.plot(kind='hist'). ** 74 | 75 | g = sns.FacetGrid(df,hue='Private',palette='coolwarm',size=6,aspect=1) 76 | g = g.map(plt.hist,'Outstate',bins=20,alpha=0.7) 77 | 78 | 79 | # Create a similar histogram for the Grad.Rate column.** 80 | 81 | g = sns.FacetGrid(df,hue='Private',palette='coolwarm',size=6,aspect=1) 82 | g = g.map(plt.hist,'Grad.Rate',bins=20,alpha=0.7) 83 | 84 | 85 | # Notice how there seems to be a private school with a graduation rate of higher than 100%.What is the name of that school? 86 | 87 | df[df['Grad.Rate']>100] 88 | 89 | # Set that school's graduation rate to 100 so it makes sense. You may get a warning not an error) when doing this operation, so use dataframe operations or just re-do the histogram visualization to make sure it actually went through. 90 | 91 | df['Grad.Rate']['Cazenovia College'] = 100 92 | 93 | df[df['Grad.Rate']>100] 94 | 95 | g = sns.FacetGrid(df,hue='Private',palette='coolwarm',size=6,aspect=1) 96 | g = g.map(plt.hist,'Grad.Rate',bins=20,alpha=0.7) 97 | 98 | # K Means Cluster Creation 99 | # Now it is time to create the Cluster labels! 100 | # 101 | # Import KMeans from SciKit Learn. 102 | 103 | from sklearn.cluster import KMeans 104 | 105 | # Create an instance of a K Means model with 2 clusters. 106 | kmeans = KMeans(n_clusters=2) 107 | 108 | # Fit the model to all the data except for the Private label. 109 | 110 | kmeans.fit(df.drop('Private',axis=1)) 111 | 112 | # What are the cluster center vectors? 113 | 114 | kmeans.cluster_centers_ 115 | 116 | # Evaluation 117 | # 118 | # There is no perfect way to evaluate clustering if you don't have the labels, however since this is just an exercise, we do have the labels, so we take advantage of this to evaluate our clusters, keep in mind, you usually won't have this luxury in the real world. 119 | # 120 | # Create a new column for df called 'Cluster', which is a 1 for a Private school, and a 0 for a public school. 121 | 122 | def conveter(private): 123 | if private == 'Yes': 124 | return 1 125 | else: 126 | return 0 127 | 128 | df['Cluster'] = df['Private'].apply(conveter) 129 | 130 | df.head() 131 | 132 | # Create a confusion matrix and classification report to see how well the Kmeans clustering worked without being given any labels. 133 | 134 | from sklearn.metrics import confusion_matrix, classification_report 135 | print (confusion_matrix(df['Cluster'],kmeans.labels_)) 136 | print("\n") 137 | print (classification_report(df['Cluster'],kmeans.labels_)) 138 | 139 | 140 | # Not so bad considering the algorithm is purely using the features to cluster the universities into 2 distinct groups! Hopefully you can begin to see how K Means is useful for clustering un-labeled data! 141 | -------------------------------------------------------------------------------- /Python_Exercises_solutions.py: -------------------------------------------------------------------------------- 1 | 2 | # coding: utf-8 3 | 4 | # # Python Crash Course Exercises 5 | # Solutions: # Pradeep K. Pant @2017 6 | # 7 | # This is an optional exercise to test your understanding of Python Basics. If you find this extremely challenging, then you probably are not ready for the rest of this course yet and don't have enough programming experience to continue. I would suggest you take another course more geared towards complete beginners, such as [Complete Python Bootcamp](https://www.udemy.com/complete-python-bootcamp) 8 | 9 | # ## Exercises 10 | # 11 | # Answer the questions or complete the tasks outlined in bold below, use the specific method described if applicable. 12 | 13 | # ** What is 7 to the power of 4?** 14 | 15 | # In[2]: 16 | 17 | print (7**4) 18 | 19 | 20 | # In[1]: 21 | 22 | 23 | 24 | 25 | # In[ ]: 26 | 27 | 28 | 29 | 30 | # In[ ]: 31 | 32 | 33 | 34 | 35 | # In[ ]: 36 | 37 | 38 | 39 | 40 | # ** Split this string:** 41 | # 42 | # s = "Hi there Sam!" 43 | # 44 | # **into a list. ** 45 | 46 | # In[5]: 47 | 48 | s = "Hi there Sam!" 49 | 50 | 51 | # In[11]: 52 | 53 | my_list = s.split() 54 | 55 | 56 | # In[12]: 57 | 58 | my_list[2] = 'dad!' 59 | 60 | 61 | # In[ ]: 62 | 63 | 64 | 65 | 66 | # In[3]: 67 | 68 | 69 | 70 | 71 | # ** Given the variables:** 72 | # 73 | # planet = "Earth" 74 | # diameter = 12742 75 | # 76 | # ** Use .format() to print the following string: ** 77 | # 78 | # The diameter of Earth is 12742 kilometers. 79 | 80 | # In[14]: 81 | 82 | planet = "Earth" 83 | diameter = 12742 84 | 85 | 86 | # In[16]: 87 | 88 | print('The diameter of {pla} is {dia} kilometers.'.format(pla=planet,dia=diameter)) 89 | 90 | 91 | # In[17]: 92 | 93 | print('The diameter of {pla} is {dia} kilometers.'.format(pla=planet,dia=diameter)) 94 | 95 | 96 | # ** Given this nested list, use indexing to grab the word "hello" ** 97 | 98 | # In[18]: 99 | 100 | lst = [1,2,[3,4],[5,[100,200,['hello']],23,11],1,7] 101 | 102 | 103 | # In[23]: 104 | 105 | lst[3][1][2][0] 106 | 107 | 108 | # In[24]: 109 | 110 | lst[3][1][2][0] 111 | 112 | 113 | # ** Given this nested dictionary grab the word "hello". Be prepared, this will be annoying/tricky ** 114 | 115 | # In[16]: 116 | 117 | d = {'k1':[1,2,3,{'tricky':['oh','man','inception',{'target':[1,2,3,'hello']}]}]} 118 | 119 | 120 | # In[30]: 121 | 122 | 123 | d = {'k1':[1,2,3,{'tricky':['oh','man','inception',{'target':[1,2,3,'hello']}]}]} 124 | 125 | 126 | # In[40]: 127 | 128 | d['k1'][3]['tricky'][3]['target'][3] 129 | 130 | 131 | # In[33]: 132 | 133 | 134 | 135 | 136 | # In[ ]: 137 | 138 | 139 | 140 | 141 | # ** What is the main difference between a tuple and a list? ** 142 | 143 | # In[23]: 144 | 145 | # Tuple is immutable 146 | 147 | 148 | # ** Create a function that grabs the email website domain from a string in the form: ** 149 | # 150 | # user@domain.com 151 | # 152 | # **So for example, passing "user@domain.com" would return: domain.com** 153 | 154 | # In[78]: 155 | 156 | def domainGet(strDomain): 157 | return strDomain.split('@')[1] 158 | 159 | 160 | # In[79]: 161 | 162 | out = domainGet('user@domain.com') 163 | 164 | 165 | # In[80]: 166 | 167 | print(out) 168 | 169 | 170 | 171 | # In[63]: 172 | 173 | domainGet('user@domain.com') 174 | 175 | 176 | # ** Create a basic function that returns True if the word 'dog' is contained in the input string. Don't worry about edge cases like a punctuation being attached to the word dog, but do account for capitalization. ** 177 | 178 | # In[200]: 179 | 180 | def findDog(st): 181 | print(st.lower()) 182 | return 'dog' in st.split() 183 | 184 | 185 | # In[201]: 186 | 187 | findDog('Is there a dog here?') 188 | 189 | 190 | # In[ ]: 191 | 192 | 193 | 194 | 195 | # ** Create a function that counts the number of times the word "dog" occurs in a string. Again ignore edge cases. ** 196 | 197 | # In[198]: 198 | 199 | def countDog(st): 200 | st.lower().split() 201 | return st.count('dog') 202 | 203 | 204 | # In[199]: 205 | 206 | countDog('This dog runs faster than the other dog dude!') 207 | 208 | 209 | # In[31]: 210 | 211 | 212 | 213 | 214 | # In[ ]: 215 | 216 | 217 | 218 | 219 | # ** Use lambda expressions and the filter() function to filter out words from a list that don't start with the letter 's'. For example:** 220 | # 221 | # seq = ['soup','dog','salad','cat','great'] 222 | # 223 | # **should be filtered down to:** 224 | # 225 | # ['soup','salad'] 226 | 227 | # In[196]: 228 | 229 | seq = ['soup','dog','salad','cat','great'] 230 | 231 | 232 | # In[197]: 233 | 234 | list(filter(lambda item:item[0]=='s',seq)) 235 | 236 | 237 | # In[35]: 238 | 239 | 240 | 241 | 242 | # In[ ]: 243 | 244 | 245 | 246 | 247 | # ### Final Problem 248 | # **You are driving a little too fast, and a police officer stops you. Write a function 249 | # to return one of 3 possible results: "No ticket", "Small ticket", or "Big Ticket". 250 | # If your speed is 60 or less, the result is "No Ticket". If speed is between 61 251 | # and 80 inclusive, the result is "Small Ticket". If speed is 81 or more, the result is "Big Ticket". Unless it is your birthday (encoded as a boolean value in the parameters of the function) -- on your birthday, your speed can be 5 higher in all 252 | # cases. ** 253 | 254 | # In[193]: 255 | 256 | def caught_speeding(speed, is_birthday): 257 | speed_org = speed 258 | if is_birthday == True: 259 | speed = speed-5 260 | else: 261 | speed = speed_org 262 | if speed <= 60: 263 | print("No Ticket") 264 | elif speed>60 and speed<81: 265 | print ("Small Ticket") 266 | elif speed>=81: 267 | print ("Big Ticket") 268 | print(speed) 269 | pass 270 | 271 | 272 | # In[195]: 273 | 274 | caught_speeding(81,False) 275 | 276 | 277 | # In[ ]: 278 | 279 | 280 | 281 | 282 | # In[42]: 283 | 284 | caught_speeding(81,True) 285 | 286 | 287 | # In[43]: 288 | 289 | caught_speeding(81,False) 290 | 291 | 292 | # # Great job! 293 | -------------------------------------------------------------------------------- /Linear_Regression_Project_Solutions.py: -------------------------------------------------------------------------------- 1 | 2 | # coding: utf-8 3 | # # Machine learning: Linear regression model 4 | # Pradeep K. Pant 5 | # ___ 6 | # 7 | # 8 | # ___ 9 | # # Linear Regression - Project Exercise 10 | # 11 | # Congratulations! You just got some contract work with an Ecommerce company based in New York City that sells clothing online but they also have in-store style and clothing advice sessions. Customers come in to the store, have sessions/meetings with a personal stylist, then they can go home and order either on a mobile app or website for the clothes they want. 12 | # 13 | # The company is trying to decide whether to focus their efforts on their mobile app experience or their website. They've hired you on contract to help them figure it out! Let's get started! 14 | # 15 | # Just follow the steps below to analyze the customer data (it's fake, don't worry I didn't give you real credit card numbers or emails). 16 | 17 | # ## Imports 18 | # ** Import pandas, numpy, matplotlib,and seaborn. Then set %matplotlib inline 19 | # (You'll import sklearn as you need it.)** 20 | 21 | 22 | import pandas as pd 23 | import numpy as np 24 | import matplotlib.pyplot as plt 25 | import seaborn as sns 26 | get_ipython().magic('matplotlib inline') 27 | 28 | 29 | # ## Get the Data 30 | # 31 | # We'll work with the Ecommerce Customers csv file from the company. It has Customer info, suchas Email, Address, and their color Avatar. Then it also has numerical value columns: 32 | # 33 | # * Avg. Session Length: Average session of in-store style advice sessions. 34 | # * Time on App: Average time spent on App in minutes 35 | # * Time on Website: Average time spent on Website in minutes 36 | # * Length of Membership: How many years the customer has been a member. 37 | # 38 | # ** Read in the Ecommerce Customers csv file as a DataFrame called customers.** 39 | 40 | customers = pd.read_csv ('Ecommerce Customers') 41 | 42 | 43 | # **Check the head of customers, and check out its info() and describe() methods.** 44 | 45 | customers.head() 46 | 47 | 48 | # more details abt data 49 | customers.describe() 50 | 51 | 52 | # Info abt data structure 53 | 54 | customers.info() 55 | 56 | 57 | # ## Exploratory Data Analysis 58 | # 59 | # **Let's explore the data!** 60 | # 61 | # For the rest of the exercise we'll only be using the numerical data of the csv file. 62 | # ___ 63 | # **Use seaborn to create a jointplot to compare the Time on Website and Yearly Amount Spent columns. Does the correlation make sense?** 64 | 65 | sns.jointplot(data=customers,x='Time on Website',y='Yearly Amount Spent') 66 | 67 | 68 | # ** Do the same but with the Time on App column instead. ** 69 | 70 | sns.jointplot(data=customers,x='Time on App',y='Yearly Amount Spent') 71 | 72 | 73 | # ** Use jointplot to create a 2D hex bin plot comparing Time on App and Length of Membership.** 74 | 75 | 76 | sns.jointplot(data=customers,x='Time on App',y='Length of Membership',kind='hex') 77 | 78 | 79 | # **Let's explore these types of relationships across the entire data set. Use [pairplot](https://stanford.edu/~mwaskom/software/seaborn/tutorial/axis_grids.html#plotting-pairwise-relationships-with-pairgrid-and-pairplot) to recreate the plot below.(Don't worry about the the colors)** 80 | 81 | sns.pairplot(customers) 82 | 83 | 84 | # **Based off this plot what looks to be the most correlated feature with Yearly Amount Spent?** 85 | 86 | # Length of Membership 87 | 88 | 89 | # **Create a linear model plot (using seaborn's lmplot) of Yearly Amount Spent vs. Length of Membership. ** 90 | 91 | 92 | sns.lmplot(data=customers, x='Length of Membership', y='Yearly Amount Spent') 93 | 94 | 95 | # ## Training and Testing Data 96 | # 97 | # Now that we've explored the data a bit, let's go ahead and split the data into training and testing sets. 98 | # ** Set a variable X equal to the numerical features of the customers and a variable y equal to the "Yearly Amount Spent" column. ** 99 | 100 | customers.columns 101 | 102 | 103 | y = customers['Yearly Amount Spent'] 104 | 105 | 106 | X = customers[['Avg. Session Length', 'Time on App', 'Time on Website', 'Length of Membership']] 107 | 108 | 109 | # ** Use model_selection.train_test_split from sklearn to split the data into training and testing sets. Set test_size=0.3 and random_state=101** 110 | 111 | from sklearn.cross_validation import train_test_split 112 | 113 | 114 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) 115 | 116 | # ## Training the Model 117 | # 118 | # Now its time to train our model on our training data! 119 | # 120 | # ** Import LinearRegression from sklearn.linear_model ** 121 | 122 | from sklearn.linear_model import LinearRegression 123 | 124 | 125 | # **Create an instance of a LinearRegression() model named lm.** 126 | 127 | lm = LinearRegression() 128 | 129 | 130 | # ** Train/fit lm on the training data.** 131 | 132 | 133 | lm.fit(X_train,y_train) 134 | 135 | 136 | # **Print out the coefficients of the model** 137 | 138 | lm.coef_ 139 | 140 | 141 | # ## Predicting Test Data 142 | # Now that we have fit our model, let's evaluate its performance by predicting off the test values! 143 | # 144 | # ** Use lm.predict() to predict off the X_test set of the data.** 145 | 146 | predictions = lm.predict(X_test) 147 | 148 | 149 | # ** Create a scatterplot of the real test values versus the predicted values. ** 150 | 151 | plt.scatter(y_test,predictions) 152 | plt.xlabel('Y Test values') 153 | plt.ylabel('Predicted values') 154 | 155 | 156 | # ## Evaluating the Model 157 | # 158 | # Let's evaluate our model performance by calculating the residual sum of squares and the explained variance score (R^2). 159 | # 160 | # ** Calculate the Mean Absolute Error, Mean Squared Error, and the Root Mean Squared Error. Refer to the lecture or to Wikipedia for the formulas** 161 | 162 | 163 | from sklearn import metrics 164 | 165 | 166 | print ('MAE',metrics.mean_absolute_error(y_test,predictions)) 167 | print ('MSE',metrics.mean_squared_error(y_test,predictions)) 168 | print ('RMSE',np.sqrt(metrics.mean_squared_error(y_test,predictions))) 169 | 170 | 171 | metrics.explained_variance_score(y_test,predictions) 172 | 173 | 174 | # ## Residuals 175 | # 176 | # You should have gotten a very good model with a good fit. Let's quickly explore the residuals to make sure everything was okay with our data. 177 | # 178 | # **Plot a histogram of the residuals and make sure it looks normally distributed. Use either seaborn distplot, or just plt.hist().** 179 | 180 | sns.distplot(y_test-predictions,bins=50) 181 | 182 | 183 | # ## Conclusion 184 | # We still want to figure out the answer to the original question, do we focus our efforst on mobile app or website development? Or maybe that doesn't even really matter, and Membership Time is what is really important. Let's see if we can interpret the coefficients at all to get an idea. 185 | # 186 | # ** Recreate the dataframe below. ** 187 | 188 | 189 | cdf = pd.DataFrame(lm.coef_,X.columns,columns=['Coeffecient']) 190 | 191 | # print variable 192 | cdf 193 | 194 | 195 | # ** How can you interpret these coefficients? ** 196 | 197 | # 198 | 199 | # **Do you think the company should focus more on their mobile app or on their website?** 200 | 201 | # 202 | # *Answer here* 203 | 204 | -------------------------------------------------------------------------------- /NLP_Project.py: -------------------------------------------------------------------------------- 1 | 2 | # coding: utf-8 3 | 4 | # Author: Pradeep K.Pant 5 | 6 | # # Natural Language Processing Project 7 | # 8 | # Welcome to the NLP Project for this section of the course. In this NLP project you will be attempting to classify Yelp Reviews into 1 star or 5 star categories based off the text content in the reviews. This will be a simpler procedure than the lecture, since we will utilize the pipeline methods for more complex tasks. 9 | # 10 | # We will use the [Yelp Review Data Set from Kaggle](https://www.kaggle.com/c/yelp-recsys-2013). 11 | # 12 | # Each observation in this dataset is a review of a particular business by a particular user. 13 | # 14 | # The "stars" column is the number of stars (1 through 5) assigned by the reviewer to the business. (Higher stars is better.) In other words, it is the rating of the business by the person who wrote the review. 15 | # 16 | # The "cool" column is the number of "cool" votes this review received from other Yelp users. 17 | # 18 | # All reviews start with 0 "cool" votes, and there is no limit to how many "cool" votes a review can receive. In other words, it is a rating of the review itself, not a rating of the business. 19 | # 20 | # The "useful" and "funny" columns are similar to the "cool" column. 21 | # 22 | # Let's get started! Just follow the directions below! 23 | 24 | # Imports essential libraries 25 | import pandas as pd 26 | import numpy as np 27 | import seaborn as sns 28 | import matplotlib.pyplot as plt 29 | # This is needed to show the plot in Jupyter notebook 30 | get_ipython().magic('matplotlib inline') 31 | 32 | # The Data 33 | # 34 | # Read the yelp.csv file and set it as a dataframe called yelp. 35 | 36 | yelp = pd.read_csv('yelp.csv') 37 | 38 | 39 | # Check the head, info , and describe methods on yelp. 40 | 41 | yelp.head() 42 | 43 | yelp.info() 44 | 45 | yelp.describe() 46 | 47 | # Create a new column called "text length" which is the number of words in the text column. 48 | 49 | yelp['text length'] = yelp['text'].apply(len) 50 | 51 | 52 | # Lets do some exploratory data analysis 53 | # 54 | # Let's explore the data 55 | # 56 | # Imports 57 | # 58 | # Import the data visualization libraries if you haven't done so already. 59 | 60 | sns.set_style('white') 61 | 62 | # Use FacetGrid from the seaborn library to create a grid of 5 histograms of text length based off of the star ratings. Reference the seaborn documentation for hints on this 63 | 64 | g = sns.FacetGrid(yelp,col='stars') 65 | g.map(plt.hist,'text length', bins=50) 66 | 67 | 68 | # Create a boxplot of text length for each star category. 69 | 70 | sns.boxplot(x='stars',y='text length',data=yelp, palette='rainbow') 71 | 72 | 73 | # Create a countplot of the number of occurrences for each type of star rating. 74 | 75 | sns.countplot(x='stars',data=yelp, palette='rainbow') 76 | 77 | # Use groupby to get the mean values of the numerical columns, you should be able to create this dataframe with the operation: 78 | 79 | stars = yelp.groupby('stars').mean() 80 | stars 81 | 82 | 83 | # To check the correlation between values, use the corr() method on that groupby dataframe to produce this dataframe: 84 | 85 | stars.corr() 86 | 87 | # Then use seaborn to create a heatmap based off that .corr() dataframe: 88 | 89 | sns.heatmap(stars.corr(),annot=True) 90 | 91 | # NLP Classification Task 92 | # Let's move on to the actual task. To make things a little easier, go ahead and only grab reviews that were either 1 star or 5 stars. 93 | # 94 | # Create a dataframe called yelp_class that contains the columns of yelp dataframe but for only the 1 or 5 star reviews. 95 | 96 | yelp_class = yelp[(yelp['stars']==1)| (yelp['stars']==5)] 97 | yelp_class.info 98 | 99 | # Create two objects X and y. X will be the 'text' column of yelp_class and y will be the 'stars' column of yelp_class. (Your features and target/labels) 100 | 101 | X = yelp_class['text'] 102 | y = yelp_class['stars'] 103 | 104 | # Import CountVectorizer and create a CountVectorizer object. 105 | 106 | from sklearn.feature_extraction.text import CountVectorizer 107 | cv = CountVectorizer() 108 | 109 | # Use the fit_transform method on the CountVectorizer object and pass in X (the 'text' column). Save this result by overwriting X. 110 | 111 | X = cv.fit_transform(X) 112 | 113 | # Train Test Split 114 | # 115 | # Let's split our data into training and testing data. 116 | # 117 | # Use train_test_split to split up the data into X_train, X_test, y_train, y_test. Use test_size=0.3 and random_state=101 118 | # Note: One can use any value for random state and test size 119 | 120 | from sklearn.cross_validation import train_test_split 121 | 122 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=101) 123 | 124 | 125 | # Training a Model 126 | 127 | # Time to train a model! 128 | # 129 | # Import MultinomialNB and create an instance of the estimator and call is nb 130 | 131 | from sklearn.naive_bayes import MultinomialNB 132 | nb = MultinomialNB() 133 | 134 | # Now fit nb using the training data. 135 | 136 | nb.fit(X_train,y_train) 137 | 138 | 139 | # Predictions and Evaluations 140 | # 141 | # Time to see how our model did! 142 | # 143 | # Use the predict method off of nb to predict labels from X_test. 144 | 145 | predictions = nb.predict(X_test) 146 | 147 | 148 | # Create a confusion matrix and classification report using these predictions and y_test 149 | 150 | from sklearn.metrics import confusion_matrix, classification_report 151 | 152 | print(confusion_matrix(y_test,predictions)) 153 | print('\n') 154 | print (classification_report(y_test,predictions)) 155 | 156 | # Now, Let's see what happens if we try to include TF-IDF to this process using a pipeline.** 157 | 158 | # Using Text Processing 159 | # Import TfidfTransformer from sklearn. 160 | 161 | from sklearn.feature_extraction.text import TfidfTransformer 162 | 163 | # Import Pipeline from sklearn. ** 164 | from sklearn.pipeline import Pipeline 165 | 166 | # Now create a pipeline with the following steps:CountVectorizer(), TfidfTransformer(),MultinomialNB() 167 | 168 | pipe = Pipeline([('bow',CountVectorizer()),('tfidf',TfidfTransformer()),('model',MultinomialNB())]) 169 | 170 | 171 | # Using the Pipeline 172 | # 173 | # Time to use the pipeline! Remember this pipeline has all your pre-process steps in it already, meaning we'll need to re-split the original data (Remember that we overwrote X as the CountVectorized version. What we need is just the text 174 | 175 | # ### Train Test Split 176 | # 177 | # Redo the train test split on the yelp_class object. 178 | 179 | X = yelp_class['text'] 180 | y = yelp_class['stars'] 181 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=101) 182 | 183 | # Now fit the pipeline to the training data. Remember you can't use the same training data as last time because that data has already been vectorized. We need to pass in just the text and labels 184 | 185 | pipe.fit(X_train,y_train) 186 | 187 | # Predictions and Evaluation 188 | # 189 | # Now use the pipeline to predict from the X_test and create a classification report and confusion matrix. You should notice strange results. 190 | 191 | predictions = pipe.predict(X_test) 192 | 193 | # Lets check again confusion matrix and classification report 194 | print(confusion_matrix(y_test,predictions)) 195 | print('\n') 196 | print (classification_report(y_test,predictions)) 197 | 198 | # Looks like Tf-Idf actually made things worse! That is it for this project. 199 | 200 | # Some other things to try... 201 | # Try going back and playing around with the pipeline steps and seeing if creating a custom analyzer helps Or recreate the pipeline with just the CountVectorizer() and NaiveBayes. Does changing the ML model at the end to another classifier help at all? -------------------------------------------------------------------------------- /Decision_Trees_and_Random_Forest_Project_source_code.py: -------------------------------------------------------------------------------- 1 | 2 | # coding: utf-8 3 | # Pradeep K. Pant 4 | # Random Forest Project 5 | # For this project we will be exploring publicly available data from [LendingClub.com](www.lendingclub.com). Lending Club connects people who need money (borrowers) with people who have money (investors). Hopefully, as an investor you would want to invest in people who showed a profile of having a high probability of paying you back. We will try to create a model that will help predict this. 6 | # 7 | # Lending club had a [very interesting year in 2016](https://en.wikipedia.org/wiki/Lending_Club#2016), so let's check out some of their data and keep the context in mind. This data is from before they even went public. 8 | # 9 | # We will use lending data from 2007-2010 and be trying to classify and predict whether or not the borrower paid back their loan in full. You can download the data from [here](https://www.lendingclub.com/info/download-data.action) or just use the csv already provided. It's recommended you use the csv provided as it has been cleaned of NA values. 10 | # 11 | # Here are what the columns represent: 12 | # * credit.policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise. 13 | # * purpose: The purpose of the loan (takes values "credit_card", "debt_consolidation", "educational", "major_purchase", "small_business", and "all_other"). 14 | # * int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates. 15 | # * installment: The monthly installments owed by the borrower if the loan is funded. 16 | # * log.annual.inc: The natural log of the self-reported annual income of the borrower. 17 | # * dti: The debt-to-income ratio of the borrower (amount of debt divided by annual income). 18 | # * fico: The FICO credit score of the borrower. 19 | # * days.with.cr.line: The number of days the borrower has had a credit line. 20 | # * revol.bal: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle). 21 | # * revol.util: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available). 22 | # * inq.last.6mths: The borrower's number of inquiries by creditors in the last 6 months. 23 | # * delinq.2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years. 24 | # * pub.rec: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments). 25 | 26 | # Import Libraries: Import the usual libraries for pandas and plotting. You can import sklearn later on 27 | 28 | import pandas as pd 29 | import numpy as np 30 | import matplotlib.pyplot as plt 31 | import seaborn as sns 32 | get_ipython().magic('matplotlib inline') 33 | 34 | 35 | #Get the Data 36 | # Use pandas to read loan_data.csv as a dataframe called loans. 37 | 38 | loans = pd.read_csv("loan_data.csv") 39 | 40 | # Check out the info(), head(), and describe() methods on loans. 41 | 42 | loans.info() 43 | 44 | loans.head() 45 | 46 | loans.describe() 47 | 48 | # Let's do some Exploratory Data Analysis 49 | # Let's do some data visualization! We'll use seaborn and pandas built-in plotting capabilities, but feel free to use whatever library you want. Don't worry about the colors matching, just worry about getting the main idea of the plot. 50 | # 51 | # Create a histogram of two FICO distributions on top of each other, one for each credit.policy outcome. 52 | 53 | # For making histogram we are just using pandas built in .hist() 54 | 55 | plt.figure(figsize=(10,6)) 56 | loans[loans['credit.policy']==1]['fico'].hist(bins=35,color='blue', alpha=0.6, 57 | label='Credit Policy = 1') 58 | loans[loans['credit.policy']==0]['fico'].hist(bins=35,color='red', alpha=0.6, 59 | label='Credit Policy = 0') 60 | plt.legend() 61 | plt.xlabel("FICO") 62 | 63 | 64 | # Create a similar figure, except this time select by the not.fully.paid column. 65 | 66 | plt.figure(figsize=(10,6)) 67 | loans[loans['not.fully.paid']==1]['fico'].hist(bins=35,color='blue', alpha=0.6, 68 | label='Not Fully Paid = 1') 69 | loans[loans['not.fully.paid']==0]['fico'].hist(bins=35,color='red', alpha=0.6, 70 | label='Not Fully Paid = 0') 71 | plt.legend() 72 | plt.xlabel("FICO") 73 | 74 | # Create a countplot using seaborn showing the counts of loans by purpose, with the color hue defined by not.fully.paid. 75 | 76 | plt.figure(figsize=(11,7)) 77 | sns.countplot(x='purpose',hue='not.fully.paid',data=loans,palette='Set1') 78 | 79 | # Let's see the trend between FICO score and interest rate. Recreate the following jointplot. 80 | 81 | sns.jointplot(x='fico', y='int.rate',data=loans, color='purple') 82 | 83 | # Create the following lmplots to see if the trend differed between not.fully.paid and credit.policy. Check the documentation for lmplot() if you can't figure out how to separate it into columns. 84 | 85 | plt.figure(figsize=(11,7)) 86 | sns.lmplot(y='int.rate',x='fico',data=loans,hue='credit.policy',col='not.fully.paid',palette='Set1') 87 | 88 | 89 | # Let's get ready to set up our data for our Random Forest Classification Model! 90 | # 91 | # Check loans.info() again. 92 | 93 | loans.info() 94 | 95 | # Categorical Features 96 | # 97 | # Notice that the purpose column as categorical 98 | # 99 | # That means we need to transform them using dummy variables so sklearn will be able to understand them. Let's do this in one clean step using pd.get_dummies. 100 | # 101 | # Let's show you a way of dealing with these columns that can be expanded to multiple categorical features if necessary. 102 | # 103 | # Create a list of 1 element containing the string 'purpose'. Call this list cat_feats. 104 | 105 | cat_feats = ['purpose'] 106 | 107 | # Now use pd.get_dummies(loans,columns=cat_feats,drop_first=True) to create a fixed larger dataframe that has new feature columns with dummy variables. Set this dataframe as final_data. 108 | 109 | final_data = pd.get_dummies(loans,columns=cat_feats,drop_first=True) 110 | 111 | final_data.head() 112 | 113 | # Train Test Split 114 | # 115 | # Now its time to split our data into a training set and a testing set! 116 | # 117 | # Use sklearn to split your data into a training set and a testing set 118 | 119 | from sklearn.cross_validation import train_test_split 120 | X = final_data.drop('not.fully.paid',axis=1) 121 | y = final_data['not.fully.paid'] 122 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101) 123 | 124 | # Training a Decision Tree Model 125 | # Let's start by training a single decision tree first! 126 | # Import DecisionTreeClassifier 127 | 128 | from sklearn.tree import DecisionTreeClassifier 129 | 130 | # Create an instance of DecisionTreeClassifier() called dtree and fit it to the training data. 131 | 132 | dtree = DecisionTreeClassifier() 133 | 134 | dtree.fit(X_train,y_train) 135 | 136 | # Predictions and Evaluation of Decision Tree 137 | # Create predictions from the test set and create a classification report and a confusion matrix. 138 | 139 | predictions = dtree.predict(X_test) 140 | 141 | from sklearn.metrics import classification_report, confusion_matrix 142 | 143 | print (classification_report(y_test,predictions)) 144 | 145 | print (confusion_matrix(y_test,predictions)) 146 | 147 | # Training the Random Forest model 148 | 149 | # Now its time to train our model! 150 | # 151 | # Create an instance of the RandomForestClassifier class and fit it to our training data from the previous step. 152 | 153 | from sklearn.ensemble import RandomForestClassifier 154 | rfc = RandomForestClassifier(n_estimators=300) 155 | rfc.fit(X_train,y_train) 156 | 157 | # Predictions and Evaluation 158 | # Let's predict off the y_test values and evaluate our model. 159 | # 160 | # Predict the class of not.fully.paid for the X_test data. 161 | 162 | predictions = rfc.predict(X_test) 163 | 164 | # Now create a classification report from the results. Do you get anything strange or some sort of warning? 165 | 166 | print (classification_report(y_test,predictions)) 167 | 168 | print (confusion_matrix(y_test,predictions)) 169 | 170 | 171 | # Show the Confusion Matrix for the predictions. 172 | 173 | # What performed better the random forest or the decision tree? 174 | # Overall Ramdom forest performed better but if you check indivisual components then Decsion tree performed well. -------------------------------------------------------------------------------- /advertising.csv: -------------------------------------------------------------------------------- 1 | Daily Time Spent on Site,Age,Area Income,Daily Internet Usage,Ad Topic Line,City,Male,Country,Timestamp,Clicked on Ad 2 | 68.95,35,61833.9,256.09,Cloned 5thgeneration orchestration,Wrightburgh,0,Tunisia,2016-03-27 00:53:11,0 3 | 80.23,31,68441.85,193.77,Monitored national standardization,West Jodi,1,Nauru,2016-04-04 01:39:02,0 4 | 69.47,26,59785.94,236.5,Organic bottom-line service-desk,Davidton,0,San Marino,2016-03-13 20:35:42,0 5 | 74.15,29,54806.18,245.89,Triple-buffered reciprocal time-frame,West Terrifurt,1,Italy,2016-01-10 02:31:19,0 6 | 68.37,35,73889.99,225.58,Robust logistical utilization,South Manuel,0,Iceland,2016-06-03 03:36:18,0 7 | 59.99,23,59761.56,226.74,Sharable client-driven software,Jamieberg,1,Norway,2016-05-19 14:30:17,0 8 | 88.91,33,53852.85,208.36,Enhanced dedicated support,Brandonstad,0,Myanmar,2016-01-28 20:59:32,0 9 | 66.0,48,24593.33,131.76,Reactive local challenge,Port Jefferybury,1,Australia,2016-03-07 01:40:15,1 10 | 74.53,30,68862.0,221.51,Configurable coherent function,West Colin,1,Grenada,2016-04-18 09:33:42,0 11 | 69.88,20,55642.32,183.82,Mandatory homogeneous architecture,Ramirezton,1,Ghana,2016-07-11 01:42:51,0 12 | 47.64,49,45632.51,122.02,Centralized neutral neural-net,West Brandonton,0,Qatar,2016-03-16 20:19:01,1 13 | 83.07,37,62491.01,230.87,Team-oriented grid-enabled Local Area Network,East Theresashire,1,Burundi,2016-05-08 08:10:10,0 14 | 69.57,48,51636.92,113.12,Centralized content-based focus group,West Katiefurt,1,Egypt,2016-06-03 01:14:41,1 15 | 79.52,24,51739.63,214.23,Synergistic fresh-thinking array,North Tara,0,Bosnia and Herzegovina,2016-04-20 21:49:22,0 16 | 42.95,33,30976.0,143.56,Grass-roots coherent extranet,West William,0,Barbados,2016-03-24 09:31:49,1 17 | 63.45,23,52182.23,140.64,Persistent demand-driven interface,New Travistown,1,Spain,2016-03-09 03:41:30,1 18 | 55.39,37,23936.86,129.41,Customizable multi-tasking website,West Dylanberg,0,Palestinian Territory,2016-01-30 19:20:41,1 19 | 82.03,41,71511.08,187.53,Intuitive dynamic attitude,Pruittmouth,0,Afghanistan,2016-05-02 07:00:58,0 20 | 54.7,36,31087.54,118.39,Grass-roots solution-oriented conglomeration,Jessicastad,1,British Indian Ocean Territory (Chagos Archipelago),2016-02-13 07:53:55,1 21 | 74.58,40,23821.72,135.51,Advanced 24/7 productivity,Millertown,1,Russian Federation,2016-02-27 04:43:07,1 22 | 77.22,30,64802.33,224.44,Object-based reciprocal knowledgebase,Port Jacqueline,1,Cameroon,2016-01-05 07:52:48,0 23 | 84.59,35,60015.57,226.54,Streamlined non-volatile analyzer,Lake Nicole,1,Cameroon,2016-03-18 13:22:35,0 24 | 41.49,52,32635.7,164.83,Mandatory disintermediate utilization,South John,0,Burundi,2016-05-20 08:49:33,1 25 | 87.29,36,61628.72,209.93,Future-proofed methodical protocol,Pamelamouth,1,Korea,2016-03-23 09:43:43,0 26 | 41.39,41,68962.32,167.22,Exclusive neutral parallelism,Harperborough,0,Tokelau,2016-06-13 17:27:09,1 27 | 78.74,28,64828.0,204.79,Public-key foreground groupware,Port Danielleberg,1,Monaco,2016-05-27 15:25:52,0 28 | 48.53,28,38067.08,134.14,Ameliorated client-driven forecast,West Jeremyside,1,Tuvalu,2016-02-08 10:46:14,1 29 | 51.95,52,58295.82,129.23,Monitored systematic hierarchy,South Cathyfurt,0,Greece,2016-07-19 08:32:10,1 30 | 70.2,34,32708.94,119.2,Open-architected impactful productivity,Palmerside,0,British Virgin Islands,2016-04-14 05:08:35,1 31 | 76.02,22,46179.97,209.82,Business-focused value-added definition,West Guybury,0,Bouvet Island (Bouvetoya),2016-01-27 12:38:16,0 32 | 67.64,35,51473.28,267.01,Programmable asymmetric data-warehouse,Phelpschester,1,Peru,2016-07-02 20:23:15,0 33 | 86.41,28,45593.93,207.48,Digitized static capability,Lake Melindamouth,1,Aruba,2016-03-01 22:13:37,0 34 | 59.05,57,25583.29,169.23,Digitized global capability,North Richardburgh,1,Maldives,2016-07-15 05:05:14,1 35 | 55.6,23,30227.98,212.58,Multi-layered 4thgeneration knowledge user,Port Cassie,0,Senegal,2016-01-14 14:00:09,1 36 | 57.64,57,45580.92,133.81,Synchronized dedicated service-desk,New Thomas,1,Dominica,2016-03-15 03:12:25,1 37 | 84.37,30,61389.5,201.58,Synchronized systemic hierarchy,Johnstad,0,Luxembourg,2016-04-12 03:26:39,0 38 | 62.26,53,56770.79,125.45,Profound stable product,West Aprilport,1,Montenegro,2016-04-07 15:18:10,1 39 | 65.82,39,76435.3,221.94,Reactive demand-driven capacity,Kellytown,0,Ukraine,2016-02-09 05:28:18,0 40 | 50.43,46,57425.87,119.32,Persevering needs-based open architecture,Charlesport,1,Saint Helena,2016-05-07 17:11:49,1 41 | 38.93,39,27508.41,162.08,Intuitive exuding service-desk,Millerchester,0,Liberia,2016-03-11 06:49:10,1 42 | 84.98,29,57691.95,202.61,Innovative user-facing extranet,Mackenziemouth,0,Russian Federation,2016-04-27 09:27:58,0 43 | 64.24,30,59784.18,252.36,Front-line intermediate database,Zacharystad,0,Tunisia,2016-04-16 11:53:43,0 44 | 82.52,32,66572.39,198.11,Persevering exuding system engine,North Joshua,1,Turkmenistan,2016-05-08 15:38:46,0 45 | 81.38,31,64929.61,212.3,Balanced dynamic application,Bowenview,0,Saint Helena,2016-02-08 00:23:38,0 46 | 80.47,25,57519.64,204.86,Reduced global support,Jamesberg,0,Niger,2016-02-11 13:26:22,0 47 | 37.68,52,53575.48,172.83,Organic leadingedge secured line,Lake Cassandraport,1,Turkmenistan,2016-02-17 13:16:33,1 48 | 69.62,20,50983.75,202.25,Business-focused encompassing neural-net,New Sharon,1,Qatar,2016-02-26 22:46:43,0 49 | 85.4,43,67058.72,198.72,Triple-buffered demand-driven alliance,Johnport,0,Sri Lanka,2016-06-08 18:54:01,0 50 | 44.33,37,52723.34,123.72,Visionary maximized process improvement,Hamiltonfort,1,Trinidad and Tobago,2016-01-08 09:32:26,1 51 | 48.01,46,54286.1,119.93,Centralized 24/7 installation,West Christopher,0,Italy,2016-04-25 11:01:54,1 52 | 73.18,23,61526.25,196.71,Organized static focus group,Hollandberg,1,British Virgin Islands,2016-04-04 07:07:46,0 53 | 79.94,28,58526.04,225.29,Visionary reciprocal circuit,Odomville,0,United Kingdom,2016-05-03 21:19:58,0 54 | 33.33,45,53350.11,193.58,Pre-emptive value-added workforce,East Samanthashire,1,Guinea-Bissau,2016-01-17 09:31:36,1 55 | 50.33,50,62657.53,133.2,Sharable analyzing alliance,South Lauraton,1,Micronesia,2016-03-02 04:57:51,1 56 | 62.31,47,62722.57,119.3,Team-oriented encompassing portal,Amandahaven,0,Turkey,2016-02-14 07:36:58,1 57 | 80.6,31,67479.62,177.55,Sharable bottom-line solution,Thomasview,0,Croatia,2016-04-07 03:56:16,0 58 | 65.19,36,75254.88,150.61,Cross-group regional website,Garciaside,0,Israel,2016-02-17 11:42:00,1 59 | 44.98,49,52336.64,129.31,Organized global model,Port Sarahshire,0,Svalbard & Jan Mayen Islands,2016-04-10 00:13:47,1 60 | 77.63,29,56113.37,239.22,Upgradable asynchronous circuit,Port Gregory,0,Azerbaijan,2016-02-14 17:05:15,0 61 | 41.82,41,24852.9,156.36,Phased transitional instruction set,Brendachester,0,Iran,2016-05-26 22:49:47,1 62 | 85.61,27,47708.42,183.43,Customer-focused empowering ability,Lake Amy,0,Burundi,2016-04-30 08:07:13,0 63 | 85.84,34,64654.66,192.93,Front-line heuristic data-warehouse,Lake Annashire,1,Saint Vincent and the Grenadines,2016-06-15 05:30:13,0 64 | 72.08,29,71228.44,169.5,Stand-alone national attitude,Smithburgh,0,Burundi,2016-03-09 14:45:33,0 65 | 86.06,32,61601.05,178.92,Focused upward-trending core,North Leonmouth,1,Bulgaria,2016-03-31 20:55:22,0 66 | 45.96,45,66281.46,141.22,Streamlined cohesive conglomeration,Robertfurt,0,Christmas Island,2016-06-03 00:55:23,1 67 | 62.42,29,73910.9,198.5,Upgradable optimizing toolset,Jasminefort,1,Canada,2016-03-10 23:36:03,0 68 | 63.89,40,51317.33,105.22,Synchronized user-facing core,Jensenborough,0,Rwanda,2016-01-08 00:17:27,1 69 | 35.33,32,51510.18,200.22,Organized client-driven alliance,Bradleyburgh,0,Turks and Caicos Islands,2016-06-05 22:11:34,1 70 | 75.74,25,61005.87,215.25,Ergonomic multi-state structure,New Sheila,1,Tunisia,2016-01-16 11:35:01,0 71 | 78.53,34,32536.98,131.72,Synergized multimedia emulation,North Regina,0,Norfolk Island,2016-04-22 20:10:22,1 72 | 46.13,31,60248.97,139.01,Customer-focused optimizing moderator,Davidmouth,0,Bouvet Island (Bouvetoya),2016-02-01 09:00:55,1 73 | 69.01,46,74543.81,222.63,Advanced full-range migration,New Michaeltown,0,Turks and Caicos Islands,2016-07-07 13:37:34,0 74 | 55.35,39,75509.61,153.17,De-engineered object-oriented protocol,East Tammie,1,Cook Islands,2016-03-08 00:37:54,1 75 | 33.21,43,42650.32,167.07,Polarized clear-thinking budgetary management,Wilcoxport,1,Turkey,2016-05-10 17:39:06,1 76 | 38.46,42,58183.04,145.98,Customizable 6thgeneration knowledge user,East Michaelmouth,1,Guatemala,2016-04-06 11:24:21,1 77 | 64.1,22,60465.72,215.93,Seamless object-oriented structure,East Tiffanyport,0,Cote d'Ivoire,2016-04-01 16:21:05,0 78 | 49.81,35,57009.76,120.06,Seamless real-time array,Ramirezhaven,1,Faroe Islands,2016-01-05 04:18:46,1 79 | 82.73,33,54541.56,238.99,Grass-roots impactful system engine,Cranemouth,1,Qatar,2016-05-20 21:31:24,0 80 | 56.14,38,32689.04,113.53,Devolved tangible approach,Lake Edward,1,Ireland,2016-02-03 07:59:16,1 81 | 55.13,45,55605.92,111.71,Customizable executive software,Lake Conniefurt,0,Ukraine,2016-02-17 21:55:29,1 82 | 78.11,27,63296.87,209.25,Progressive analyzing attitude,East Shawnchester,1,Moldova,2016-01-30 16:10:04,0 83 | 73.46,28,65653.47,222.75,Innovative executive encoding,West Joseph,1,Nicaragua,2016-05-15 14:41:49,0 84 | 56.64,38,61652.53,115.91,Down-sized uniform info-mediaries,Lake Christopherfurt,0,Montserrat,2016-01-05 17:56:52,1 85 | 68.94,54,30726.26,138.71,Streamlined next generation implementation,East Tylershire,0,Timor-Leste,2016-04-19 07:34:28,1 86 | 70.79,31,74535.94,184.1,Distributed tertiary system engine,Sharpberg,0,Bouvet Island (Bouvetoya),2016-03-15 15:49:14,0 87 | 57.76,41,47861.93,105.15,Triple-buffered scalable groupware,Lake Dustin,0,Puerto Rico,2016-06-12 15:25:44,1 88 | 77.51,36,73600.28,200.55,Total 5thgeneration encoding,North Kristine,0,Central African Republic,2016-07-01 04:41:57,0 89 | 52.7,34,58543.94,118.6,Integrated human-resource encoding,Grahamberg,1,Venezuela,2016-05-08 12:12:04,1 90 | 57.7,34,42696.67,109.07,Phased dynamic customer loyalty,New Tina,0,Australia,2016-03-14 23:13:11,1 91 | 56.89,37,37334.78,109.29,Open-source coherent policy,Nelsonfurt,1,Wallis and Futuna,2016-05-25 00:19:57,1 92 | 69.9,43,71392.53,138.35,Down-sized modular intranet,Christopherport,0,Jersey,2016-05-13 11:51:10,1 93 | 55.79,24,59550.05,149.67,Pre-emptive content-based focus group,Port Sarahhaven,0,Puerto Rico,2016-02-20 20:47:05,1 94 | 70.03,26,64264.25,227.72,Versatile 4thgeneration system engine,Bradleyborough,1,Samoa,2016-05-22 20:49:37,0 95 | 50.08,40,64147.86,125.85,Ergonomic full-range time-frame,Whiteport,1,Greece,2016-04-10 02:02:36,1 96 | 43.67,31,25686.34,166.29,Automated directional function,New Theresa,1,Antarctica (the territory South of 60 deg S),2016-02-28 06:41:44,1 97 | 72.84,26,52968.22,238.63,Progressive empowering alliance,Wongland,0,Albania,2016-07-08 21:18:32,0 98 | 45.72,36,22473.08,154.02,Versatile homogeneous capacity,Williammouth,1,Hong Kong,2016-04-19 15:14:58,1 99 | 39.94,41,64927.19,156.3,Function-based optimizing protocol,Williamsborough,0,Lithuania,2016-01-08 22:47:10,1 100 | 35.61,46,51868.85,158.22,Up-sized secondary software,North Michael,0,Egypt,2016-03-28 08:46:26,1 101 | 79.71,34,69456.83,211.65,Seamless holistic time-frame,Benjaminchester,1,Bangladesh,2016-07-02 14:57:53,0 102 | 41.49,53,31947.65,169.18,Persevering reciprocal firmware,Hernandezville,0,Western Sahara,2016-07-03 09:22:30,1 103 | 63.6,23,51864.77,235.28,Centralized logistical secured line,Youngburgh,1,Serbia,2016-06-01 09:27:34,0 104 | 89.91,40,59593.56,194.23,Innovative background conglomeration,Wallacechester,0,Maldives,2016-07-09 14:55:36,0 105 | 68.18,21,48376.14,218.17,Switchable 3rdgeneration hub,Sanchezmouth,1,Czech Republic,2016-02-09 22:04:54,0 106 | 66.49,20,56884.74,202.16,Polarized 6thgeneration info-mediaries,Bradshawborough,0,Guernsey,2016-06-10 11:31:33,0 107 | 80.49,40,67186.54,229.12,Balanced heuristic approach,Amyhaven,1,Tanzania,2016-02-14 03:50:52,0 108 | 72.23,25,46557.92,241.03,Focused 24hour implementation,Marcushaven,1,Bhutan,2016-07-05 17:17:49,0 109 | 42.39,42,66541.05,150.99,De-engineered mobile infrastructure,Erinton,0,Christmas Island,2016-04-28 05:50:25,1 110 | 47.53,30,33258.09,135.18,Customer-focused upward-trending contingency,Hughesport,0,Guinea,2016-04-03 05:10:31,1 111 | 74.02,32,72272.9,210.54,Operative system-worthy protocol,Johnstad,0,Micronesia,2016-03-09 14:57:11,0 112 | 66.63,60,60333.38,176.98,User-friendly upward-trending intranet,New Lucasburgh,0,Madagascar,2016-01-16 23:37:51,1 113 | 63.24,53,65229.13,235.78,Future-proofed holistic superstructure,Michelleside,1,Lebanon,2016-07-03 04:33:41,1 114 | 71.0,22,56067.38,211.87,Extended systemic policy,Andersonton,0,Eritrea,2016-03-14 06:46:14,0 115 | 46.13,46,37838.72,123.64,Horizontal hybrid challenge,New Rachel,1,Guyana,2016-01-09 05:44:56,1 116 | 69.0,32,72683.35,221.21,Virtual composite model,Port Susan,1,Trinidad and Tobago,2016-02-11 04:37:34,0 117 | 76.99,31,56729.78,244.34,Switchable mobile framework,West Angelabury,1,Jersey,2016-06-22 07:33:21,0 118 | 72.6,55,66815.54,162.95,Focused intangible moderator,Port Christopherborough,0,United Arab Emirates,2016-07-13 16:12:24,1 119 | 61.88,42,60223.52,112.19,Balanced actuating moderator,Phillipsbury,1,Martinique,2016-07-23 11:46:28,1 120 | 84.45,50,29727.79,207.18,Customer-focused transitional strategy,Millerside,0,Somalia,2016-07-13 04:10:53,1 121 | 88.97,45,49269.98,152.49,Advanced web-enabled standardization,Lake Jessica,0,Bhutan,2016-06-11 18:32:12,1 122 | 86.19,31,57669.41,210.26,Pre-emptive executive knowledgebase,Lopezmouth,1,Greece,2016-05-08 12:51:00,0 123 | 49.58,26,56791.75,231.94,Self-enabling holistic process improvement,Johnsport,0,Benin,2016-04-07 16:02:02,0 124 | 77.65,27,63274.88,212.79,Horizontal client-driven hierarchy,South Ronald,0,Papua New Guinea,2016-02-04 13:30:32,0 125 | 37.75,36,35466.8,225.24,Polarized dynamic throughput,South Daniel,0,Uzbekistan,2016-02-26 19:48:23,1 126 | 62.33,43,68787.09,127.11,Devolved zero administration intranet,Suzannetown,0,South Africa,2016-06-21 13:15:21,1 127 | 79.57,31,61227.59,230.93,User-friendly asymmetric info-mediaries,Lisaberg,0,Egypt,2016-05-17 04:27:31,0 128 | 80.31,44,56366.88,127.07,Cross-platform regional task-force,Brianfurt,0,Hungary,2016-04-18 15:54:33,1 129 | 89.05,45,57868.44,206.98,Polarized bandwidth-monitored moratorium,Stewartbury,0,Falkland Islands (Malvinas),2016-04-03 10:07:56,0 130 | 70.41,27,66618.21,223.03,Centralized systematic knowledgebase,Benjaminchester,0,Dominica,2016-04-04 21:30:46,0 131 | 67.36,37,73104.47,233.56,Future-proofed grid-enabled implementation,North Wesleychester,0,Jersey,2016-07-06 16:00:33,0 132 | 46.98,50,21644.91,175.37,Down-sized well-modulated archive,East Michelleberg,0,Lithuania,2016-05-04 09:00:24,1 133 | 41.67,36,53817.02,132.55,Realigned zero tolerance emulation,Port Eric,0,Saint Martin,2016-06-13 18:50:00,1 134 | 51.24,36,76368.31,176.73,Versatile transitional monitoring,Timothyfurt,0,Cuba,2016-01-03 16:01:40,1 135 | 75.7,29,67633.44,215.44,Profound zero administration instruction set,Port Jeffrey,0,United States Minor Outlying Islands,2016-01-14 00:23:10,0 136 | 43.49,47,50335.46,127.83,User-centric intangible task-force,Guzmanland,0,Belize,2016-01-12 10:07:29,1 137 | 49.89,39,17709.98,160.03,Enhanced system-worthy application,East Michele,1,Belize,2016-04-16 12:09:25,1 138 | 38.37,36,41229.16,140.46,Multi-layered user-facing paradigm,East John,0,Antarctica (the territory South of 60 deg S),2016-05-13 06:09:28,1 139 | 38.52,38,42581.23,137.28,Customer-focused 24/7 concept,Lesliebury,1,Saint Vincent and the Grenadines,2016-03-27 23:59:06,1 140 | 71.89,23,61617.98,172.81,Function-based transitional complexity,Patriciahaven,1,Kuwait,2016-02-03 23:47:56,0 141 | 75.8,38,70575.6,146.19,Progressive clear-thinking open architecture,Ashleychester,1,Thailand,2016-04-18 11:23:05,0 142 | 83.86,31,64122.36,190.25,Up-sized executive moderator,Lake Josetown,0,Gibraltar,2016-02-05 19:06:01,0 143 | 37.51,30,52097.32,163.0,Re-contextualized optimal service-desk,Debraburgh,1,Holy See (Vatican City State),2016-03-21 18:46:41,1 144 | 55.6,44,65953.76,124.38,Fully-configurable neutral open system,New Debbiestad,1,Korea,2016-06-14 11:59:58,1 145 | 83.67,44,60192.72,234.26,Upgradable system-worthy array,West Shaun,1,Saint Helena,2016-02-06 23:08:57,0 146 | 69.08,41,77460.07,210.6,Ergonomic client-driven application,Kimberlyhaven,0,Turks and Caicos Islands,2016-03-12 01:39:19,0 147 | 37.47,44,45716.48,141.89,Realigned content-based leverage,Port Lawrence,1,Czech Republic,2016-01-26 03:56:18,1 148 | 56.04,49,65120.86,128.95,Decentralized real-time circuit,West Ricardo,1,Netherlands,2016-02-07 08:02:31,1 149 | 70.92,41,49995.63,108.16,Polarized modular function,Lake Jose,1,Belarus,2016-05-05 07:58:22,1 150 | 49.78,46,71718.51,152.24,Enterprise-wide client-driven contingency,Heatherberg,0,Dominica,2016-06-29 02:43:29,1 151 | 68.61,57,61770.34,150.29,Diverse modular interface,South George,0,South Africa,2016-04-10 19:48:01,1 152 | 58.18,25,69112.84,176.28,Polarized analyzing concept,Tinachester,1,New Zealand,2016-02-10 06:37:56,0 153 | 78.54,35,72524.86,172.1,Multi-channeled asynchronous open system,Port Jodi,0,Togo,2016-05-28 20:41:50,0 154 | 37.0,48,36782.38,158.22,Function-based context-sensitive secured line,Jonathantown,1,Kenya,2016-03-24 06:36:52,1 155 | 65.4,33,66699.12,247.31,Adaptive 24hour Graphic Interface,Sylviaview,0,Palau,2016-02-12 22:51:08,0 156 | 79.52,27,64287.78,183.48,Automated coherent flexibility,East Timothyport,1,Timor-Leste,2016-06-10 10:11:00,0 157 | 87.98,38,56637.59,222.11,Focused scalable complexity,West Roytown,1,Cambodia,2016-03-31 10:44:46,0 158 | 44.64,36,55787.58,127.01,Up-sized incremental encryption,Codyburgh,0,Belize,2016-02-14 06:51:43,1 159 | 41.73,28,61142.33,202.18,Sharable dedicated Graphic Interface,Port Erikhaven,1,Cuba,2016-01-07 19:16:05,1 160 | 80.46,27,61625.87,207.96,Digitized zero administration paradigm,Port Chasemouth,1,Costa Rica,2016-02-04 02:13:52,0 161 | 75.55,36,73234.87,159.24,Managed grid-enabled standardization,Ramirezside,0,Liechtenstein,2016-05-09 02:58:58,1 162 | 76.32,35,74166.24,195.31,Networked foreground definition,East Michaeltown,1,Korea,2016-06-23 00:16:02,0 163 | 82.68,33,62669.59,222.77,Re-engineered exuding frame,West Courtney,1,Ukraine,2016-06-20 09:35:02,0 164 | 72.01,31,57756.89,251.0,Horizontal multi-state interface,West Michaelhaven,0,Angola,2016-02-29 12:31:57,0 165 | 75.83,24,58019.64,162.44,Diverse stable circuit,Walshhaven,0,Nauru,2016-01-17 15:10:31,0 166 | 41.28,50,50960.08,140.39,Universal 24/7 implementation,East Rachelview,0,Equatorial Guinea,2016-01-29 03:54:19,1 167 | 34.66,32,48246.6,194.83,Customer-focused multi-tasking Internet solution,Curtisport,0,Mongolia,2016-07-14 12:07:10,1 168 | 66.18,55,28271.84,143.42,Vision-oriented contextually-based extranet,Frankbury,0,Svalbard & Jan Mayen Islands,2016-01-10 23:14:30,1 169 | 86.06,31,53767.12,219.72,Extended local methodology,Timothytown,1,Timor-Leste,2016-04-28 18:34:56,0 170 | 59.59,42,43662.1,104.78,Re-engineered demand-driven capacity,Samanthaland,1,Brazil,2016-07-06 18:36:01,1 171 | 86.69,34,62238.58,198.56,Customer-focused attitude-oriented instruction set,South Jennifer,0,Chad,2016-05-27 06:19:27,0 172 | 43.77,52,49030.03,138.55,Synergized hybrid time-frame,Kyleborough,1,Portugal,2016-01-25 07:39:41,1 173 | 71.84,47,76003.47,199.79,Advanced exuding conglomeration,North Randy,1,Malawi,2016-05-08 22:47:18,0 174 | 80.23,31,68094.85,196.23,Secured clear-thinking middleware,South Daniellefort,0,Qatar,2016-03-19 14:23:45,0 175 | 74.41,26,64395.85,163.05,Right-sized value-added initiative,Dianashire,0,Singapore,2016-07-23 04:37:05,0 176 | 63.36,48,70053.27,137.43,Centralized tertiary pricing structure,East Eric,0,Guinea,2016-06-23 01:22:43,1 177 | 71.74,35,72423.97,227.56,Multi-channeled reciprocal artificial intelligence,Hammondport,0,Kazakhstan,2016-07-19 18:06:22,0 178 | 60.72,44,42995.8,105.69,Synergized context-sensitive database,Jacobstad,0,Kuwait,2016-02-28 18:52:44,1 179 | 72.04,22,60309.58,199.43,Realigned systematic function,Hernandezfort,0,Rwanda,2016-02-10 06:52:07,0 180 | 44.57,31,38349.78,133.17,Adaptive context-sensitive application,Joneston,1,China,2016-03-27 09:11:10,1 181 | 85.86,34,63115.34,208.23,Networked high-level structure,New Jeffreychester,0,Bouvet Island (Bouvetoya),2016-05-23 02:15:04,0 182 | 39.85,38,31343.39,145.96,Profit-focused dedicated utilization,East Stephen,0,Vietnam,2016-01-03 03:22:15,1 183 | 84.53,27,40763.13,168.34,Stand-alone tangible moderator,Turnerchester,0,Guatemala,2016-01-04 21:48:38,1 184 | 62.95,60,36752.24,157.04,Polarized tangible collaboration,Youngfort,0,Peru,2016-05-24 13:30:38,1 185 | 67.58,41,65044.59,255.61,Focused high-level conglomeration,Ingramberg,1,Mayotte,2016-02-01 19:42:40,0 186 | 85.56,29,53673.08,210.46,Advanced modular Local Area Network,South Denisefurt,0,Samoa,2016-06-05 13:16:24,0 187 | 46.88,54,43444.86,136.64,Virtual scalable secured line,Port Melissaberg,0,Singapore,2016-02-04 08:53:37,1 188 | 46.31,57,44248.52,153.98,Front-line fault-tolerant intranet,Bernardton,1,Jamaica,2016-03-24 13:37:53,1 189 | 77.95,31,62572.88,233.65,Inverse asymmetric instruction set,Port Mathew,1,Bahamas,2016-06-02 21:02:22,0 190 | 84.73,30,39840.55,153.76,Synchronized leadingedge help-desk,Aliciatown,0,Canada,2016-02-21 07:42:48,1 191 | 39.86,36,32593.59,145.85,Total 5thgeneration standardization,Josephstad,0,Algeria,2016-06-26 17:16:26,1 192 | 50.08,30,41629.86,123.91,Sharable grid-enabled matrix,West Ericfurt,0,Fiji,2016-01-03 05:34:33,1 193 | 60.23,35,43313.73,106.86,Balanced asynchronous hierarchy,New Brendafurt,0,Kenya,2016-03-08 18:00:43,1 194 | 60.7,49,42993.48,110.57,Monitored object-oriented Graphic Interface,Port Julie,1,Argentina,2016-06-19 03:19:44,1 195 | 43.67,53,46004.31,143.79,Cloned analyzing artificial intelligence,South Tiffanyton,1,Bouvet Island (Bouvetoya),2016-07-21 21:16:35,1 196 | 77.2,33,49325.48,254.05,Persistent homogeneous framework,North Elizabeth,1,Philippines,2016-02-12 20:36:40,0 197 | 71.86,32,51633.34,116.53,Face-to-face even-keeled website,Kentmouth,0,Senegal,2016-05-17 06:14:20,1 198 | 44.78,45,63363.04,137.24,Extended context-sensitive monitoring,West Casey,1,Suriname,2016-07-09 11:04:54,1 199 | 78.57,36,64045.93,239.32,Exclusive client-driven model,East Henry,1,Liberia,2016-03-27 02:35:29,0 200 | 73.41,31,73049.3,201.26,Profound executive flexibility,Hollyfurt,1,Guam,2016-01-16 08:01:40,0 201 | 77.05,27,66624.6,191.14,Reduced bi-directional strategy,North Anna,0,United Arab Emirates,2016-01-21 23:48:29,0 202 | 66.4,40,77567.85,214.42,Digitized heuristic solution,Port Destiny,0,Antigua and Barbuda,2016-06-05 00:29:13,0 203 | 69.35,29,53431.35,252.77,Seamless 4thgeneration contingency,Ianmouth,1,Argentina,2016-02-13 15:37:36,0 204 | 35.65,40,31265.75,172.58,Seamless intangible secured line,North Johntown,1,Georgia,2016-05-10 07:22:37,1 205 | 70.04,31,74780.74,183.85,Intuitive radical forecast,Hannahside,1,Jordan,2016-03-27 03:59:26,0 206 | 69.78,29,70410.11,218.79,Multi-layered non-volatile Graphical User Interface,Wilsonburgh,0,Saudi Arabia,2016-05-24 18:35:58,0 207 | 58.22,29,37345.24,120.9,User-friendly client-server instruction set,North Russellborough,0,South Africa,2016-02-11 02:40:02,1 208 | 76.9,28,66107.84,212.67,Synchronized multimedia model,Murphymouth,0,Croatia,2016-04-22 08:31:24,0 209 | 84.08,30,62336.39,187.36,Face-to-face intermediate approach,Carterburgh,1,Fiji,2016-01-13 02:58:27,0 210 | 59.51,58,39132.64,140.83,Assimilated fault-tolerant hub,Penatown,0,Australia,2016-06-16 02:01:24,1 211 | 40.15,38,38745.29,134.88,Exclusive disintermediate task-force,Joechester,1,Sao Tome and Principe,2016-06-27 18:37:04,1 212 | 76.81,28,65172.22,217.85,Managed zero tolerance concept,East Paul,1,Fiji,2016-07-03 12:57:03,0 213 | 41.89,38,68519.96,163.38,Compatible systemic function,Hartmanchester,0,Cyprus,2016-02-03 04:21:14,1 214 | 76.87,27,54774.77,235.35,Configurable fault-tolerant monitoring,Mcdonaldfort,1,Kyrgyz Republic,2016-05-29 21:17:10,0 215 | 67.28,43,76246.96,155.8,Future-proofed coherent hardware,North Mercedes,1,Pakistan,2016-04-03 21:13:46,1 216 | 81.98,40,65461.92,229.22,Ameliorated upward-trending definition,Taylorberg,0,Seychelles,2016-04-15 11:51:14,0 217 | 66.01,23,34127.21,151.95,Front-line tangible alliance,Hansenmouth,0,Samoa,2016-06-21 03:14:41,1 218 | 61.57,53,35253.98,125.94,Progressive 24hour forecast,Bradyfurt,1,Bulgaria,2016-03-14 14:13:05,1 219 | 53.3,34,44893.71,111.94,Self-enabling optimal initiative,West Jessicahaven,0,Mauritania,2016-05-06 21:07:31,1 220 | 34.87,40,59621.02,200.23,Configurable logistical Graphical User Interface,Davilachester,0,Czech Republic,2016-06-12 17:52:43,1 221 | 43.6,38,20856.54,170.49,Virtual bandwidth-monitored initiative,North Ricardotown,0,Chile,2016-01-11 07:36:22,1 222 | 77.88,37,55353.41,254.57,Multi-tiered human-resource structure,Melissafurt,0,Poland,2016-07-02 00:24:22,0 223 | 75.83,27,67516.07,200.59,Managed upward-trending instruction set,East Brianberg,0,Estonia,2016-03-04 10:13:48,0 224 | 49.95,39,68737.75,136.59,Cloned object-oriented benchmark,Millerbury,0,Turkmenistan,2016-03-24 09:12:52,1 225 | 60.94,41,76893.84,154.97,Fundamental fault-tolerant neural-net,Garciaview,0,Latvia,2016-02-14 07:30:24,1 226 | 89.15,42,59886.58,171.07,Phased zero administration success,Townsendfurt,0,Fiji,2016-04-25 07:30:21,0 227 | 78.7,30,53441.69,133.99,Compatible intangible customer loyalty,Williamstad,0,Turkey,2016-02-10 19:20:51,1 228 | 57.35,29,41356.31,119.84,Distributed 3rdgeneration definition,West Connor,0,Kazakhstan,2016-04-23 14:34:38,1 229 | 34.86,38,49942.66,154.75,Pre-emptive cohesive budgetary management,West Justin,0,Bahrain,2016-06-18 17:56:32,1 230 | 70.68,31,74430.08,199.08,Configurable multi-state utilization,Robertbury,0,Colombia,2016-07-17 01:58:53,0 231 | 76.06,23,58633.63,201.04,Diverse multi-tasking parallelism,New Tinamouth,0,Brunei Darussalam,2016-04-27 04:28:17,0 232 | 66.67,33,72707.87,228.03,Horizontal content-based synergy,Turnerview,1,Taiwan,2016-04-21 20:29:35,0 233 | 46.77,32,31092.93,136.4,Multi-tiered maximized archive,Reneechester,1,Serbia,2016-03-23 06:00:15,1 234 | 62.42,38,74445.18,143.94,Diverse executive groupware,West Tinashire,0,Saint Pierre and Miquelon,2016-07-19 07:59:18,1 235 | 78.32,28,49309.14,239.52,Synergized cohesive array,Jamesfurt,0,Australia,2016-06-26 11:52:18,1 236 | 37.32,50,56735.14,199.25,Versatile dedicated software,New Nancy,1,Chad,2016-03-30 23:40:52,1 237 | 40.42,45,40183.75,133.9,Stand-alone reciprocal synergy,Lisamouth,1,Norway,2016-03-16 07:59:37,1 238 | 76.77,36,58348.41,123.51,Universal even-keeled analyzer,Harveyport,0,Turks and Caicos Islands,2016-05-04 00:01:33,1 239 | 65.65,30,72209.99,158.05,Up-sized tertiary contingency,Ramosstad,0,Finland,2016-07-02 21:22:23,0 240 | 74.32,33,62060.11,128.17,Monitored real-time superstructure,North Kevinside,0,South Africa,2016-05-23 21:14:38,1 241 | 73.27,32,67113.46,234.75,Streamlined analyzing initiative,Haleview,1,Martinique,2016-01-29 20:16:54,0 242 | 80.03,44,24030.06,150.84,Automated static concept,Christinetown,0,Afghanistan,2016-07-23 14:47:23,1 243 | 53.68,47,56180.93,115.26,Operative stable moderator,New Michael,1,Micronesia,2016-02-16 09:11:27,1 244 | 85.84,32,62204.93,192.85,Up-sized 6thgeneration moratorium,Jonesland,1,French Southern Territories,2016-06-09 21:43:05,0 245 | 85.03,30,60372.64,204.52,Expanded clear-thinking core,North Shannon,0,Philippines,2016-06-19 09:24:35,0 246 | 70.44,24,65280.16,178.75,Polarized attitude-oriented superstructure,New Sonialand,1,Algeria,2016-06-06 21:26:51,0 247 | 81.22,53,34309.24,223.09,Networked coherent interface,Port Jason,1,San Marino,2016-01-07 13:25:21,0 248 | 39.96,45,59610.81,146.13,Enhanced homogeneous moderator,East Barbara,1,Guernsey,2016-04-15 06:08:35,1 249 | 57.05,41,50278.89,269.96,Seamless full-range website,Port Erinberg,1,Sierra Leone,2016-01-09 03:45:19,1 250 | 42.44,56,43450.11,168.27,Profit-focused attitude-oriented task-force,Petersonfurt,0,Tajikistan,2016-02-10 15:23:17,1 251 | 62.2,25,25408.21,161.16,Cross-platform multimedia algorithm,New Lindaberg,0,Liechtenstein,2016-04-24 13:42:15,1 252 | 76.7,36,71136.49,222.25,Open-source coherent monitoring,West Russell,0,Ecuador,2016-06-12 05:31:19,0 253 | 61.22,45,63883.81,119.03,Streamlined logistical secured line,South Adam,1,Switzerland,2016-01-05 09:42:22,1 254 | 84.54,33,64902.47,204.02,Synchronized stable complexity,North Tracyport,1,Moldova,2016-03-02 10:07:43,0 255 | 46.08,30,66784.81,164.63,Synergistic value-added extranet,Brownport,1,Finland,2016-07-21 10:54:35,1 256 | 56.7,48,62784.85,123.13,Progressive non-volatile neural-net,Port Crystal,0,France,2016-01-09 04:53:22,1 257 | 81.03,28,63727.5,201.15,Persevering tertiary capability,Masonhaven,0,Venezuela,2016-01-06 13:20:01,0 258 | 80.91,32,61608.23,231.42,Enterprise-wide bi-directional secured line,Derrickhaven,0,Cuba,2016-01-31 04:10:20,0 259 | 40.06,38,56782.18,138.68,Organized contextually-based customer loyalty,Olsonstad,1,Peru,2016-06-11 08:38:16,1 260 | 83.47,39,64447.77,226.11,Total directional approach,New Brandy,0,Turkey,2016-05-15 20:48:40,0 261 | 73.84,31,42042.95,121.05,Programmable uniform productivity,South Jasminebury,0,Albania,2016-06-18 17:23:26,1 262 | 74.65,28,67669.06,212.56,Robust transitional ability,East Timothy,0,French Southern Territories,2016-03-17 05:00:12,0 263 | 60.25,35,54875.95,109.77,De-engineered fault-tolerant database,Charlottefort,0,Papua New Guinea,2016-06-29 13:35:05,1 264 | 59.21,35,73347.67,144.62,Managed disintermediate matrices,Lake Beckyburgh,1,Liechtenstein,2016-02-02 08:55:26,1 265 | 43.02,44,50199.77,125.22,Configurable bottom-line application,West Lindseybury,0,Thailand,2016-04-13 05:42:52,1 266 | 84.04,38,50723.67,244.55,Self-enabling didactic pricing structure,West Alyssa,0,Malaysia,2016-07-20 09:27:24,0 267 | 70.66,43,63450.96,120.95,Versatile scalable encryption,Lake Craigview,1,Mauritius,2016-02-26 04:57:14,1 268 | 70.58,26,56694.12,136.94,Proactive next generation knowledge user,Lake David,0,Algeria,2016-02-26 09:18:48,1 269 | 72.44,34,70547.16,230.14,Customizable tangible hierarchy,Bruceburgh,0,Christmas Island,2016-04-15 14:45:48,0 270 | 40.17,26,47391.95,171.31,Visionary asymmetric encryption,South Lauratown,1,Japan,2016-02-01 14:37:34,1 271 | 79.15,26,62312.23,203.23,Intuitive explicit conglomeration,Port Robin,0,Greenland,2016-01-20 19:09:37,0 272 | 44.49,53,63100.13,168.0,Business-focused real-time toolset,Jacksonburgh,1,Sao Tome and Principe,2016-04-23 06:28:43,1 273 | 73.04,37,73687.5,221.79,Organic contextually-based focus group,Erinmouth,1,Senegal,2016-06-19 22:26:16,0 274 | 76.28,33,52686.47,254.34,Right-sized asynchronous website,Port Aliciabury,0,Guadeloupe,2016-02-15 07:55:10,0 275 | 68.88,37,78119.5,179.58,Advanced 5thgeneration capability,Port Whitneyhaven,0,Belgium,2016-02-09 19:37:52,0 276 | 73.1,28,57014.84,242.37,Universal asymmetric archive,Jeffreyshire,0,Israel,2016-01-25 07:52:53,0 277 | 47.66,29,27086.4,156.54,Devolved responsive structure,Tinaton,0,Honduras,2016-07-18 11:33:31,1 278 | 87.3,35,58337.18,216.87,Triple-buffered regional toolset,North Loriburgh,0,Estonia,2016-01-09 07:28:16,0 279 | 89.34,32,50216.01,177.78,Object-based executive productivity,Wendyton,1,Paraguay,2016-03-21 21:15:54,0 280 | 81.37,26,53049.44,156.48,Business-focused responsive website,Lake Jacqueline,1,Kyrgyz Republic,2016-02-15 12:25:28,0 281 | 81.67,28,62927.96,196.76,Visionary analyzing structure,North Christopher,1,Mauritania,2016-03-04 08:48:29,0 282 | 46.37,52,32847.53,144.27,De-engineered solution-oriented open architecture,Alexanderfurt,0,French Guiana,2016-01-05 00:02:53,1 283 | 54.88,24,32006.82,148.61,Customizable modular Internet solution,West Pamela,0,Northern Mariana Islands,2016-05-15 01:03:06,1 284 | 40.67,35,48913.07,133.18,Stand-alone encompassing throughput,West Amanda,0,Lebanon,2016-05-05 09:28:36,1 285 | 71.76,35,69285.69,237.39,Customizable zero-defect matrix,South Tomside,0,Saint Pierre and Miquelon,2016-05-26 13:18:30,0 286 | 47.51,51,53700.57,130.41,Managed well-modulated collaboration,Bethburgh,1,American Samoa,2016-05-21 01:36:16,1 287 | 75.15,22,52011.0,212.87,Universal global intranet,Jamiefort,1,Austria,2016-05-04 12:06:18,0 288 | 56.01,26,46339.25,127.26,Re-engineered real-time success,Garciamouth,0,Tonga,2016-07-05 18:59:45,1 289 | 82.87,37,67938.77,213.36,Front-line fresh-thinking open system,West Brenda,0,Tonga,2016-06-28 20:13:41,0 290 | 45.05,42,66348.95,141.36,Digitized contextually-based product,South Kyle,0,French Southern Territories,2016-05-05 11:09:29,1 291 | 60.53,24,66873.9,167.22,Organic interactive support,Combsstad,0,Serbia,2016-03-25 15:17:39,1 292 | 50.52,31,72270.88,171.62,Function-based stable alliance,Lake Allenville,0,New Caledonia,2016-01-23 15:02:13,1 293 | 84.71,32,61610.05,210.23,Reactive responsive emulation,Greenechester,0,Taiwan,2016-05-29 07:29:27,0 294 | 55.2,39,76560.59,159.46,Exclusive zero tolerance alliance,Jordantown,1,United States of America,2016-05-30 07:36:31,1 295 | 81.61,33,62667.51,228.76,Enterprise-wide local matrices,Gravesport,0,Morocco,2016-04-17 15:46:03,0 296 | 71.55,36,75687.46,163.99,Inverse next generation moratorium,South Troy,1,Suriname,2016-07-20 23:08:28,0 297 | 82.4,36,66744.65,218.97,Implemented bifurcated workforce,Lake Patrick,1,Macedonia,2016-06-29 03:07:51,0 298 | 73.95,35,67714.82,238.58,Persevering even-keeled help-desk,Millerland,0,Wallis and Futuna,2016-04-10 14:48:35,0 299 | 72.07,31,69710.51,226.45,Grass-roots eco-centric instruction set,Port Jessicamouth,0,Chile,2016-04-16 16:38:35,0 300 | 80.39,31,66269.49,214.74,Fully-configurable incremental Graphical User Interface,Paulport,0,Gabon,2016-05-03 08:21:23,0 301 | 65.8,25,60843.32,231.49,Expanded radical software,Clineshire,1,Gabon,2016-03-18 16:04:59,0 302 | 69.97,28,55041.6,250.0,Mandatory 3rdgeneration moderator,Cynthiaside,0,Holy See (Vatican City State),2016-05-22 00:01:58,0 303 | 52.62,50,73863.25,176.52,Enterprise-wide foreground emulation,Port Juan,0,Seychelles,2016-02-01 20:30:35,1 304 | 39.25,39,62378.05,152.36,Customer-focused incremental system engine,Michellefort,0,Mayotte,2016-01-23 17:39:06,1 305 | 77.56,38,63336.85,130.83,Right-sized multi-tasking solution,Port Angelamouth,1,Uganda,2016-05-19 03:52:24,1 306 | 33.52,43,42191.61,165.56,Vision-oriented optimizing middleware,Jessicahaven,0,Cambodia,2016-05-09 21:54:38,1 307 | 79.81,24,56194.56,178.85,Proactive context-sensitive project,North Daniel,1,Antigua and Barbuda,2016-05-31 11:44:45,1 308 | 84.79,33,61771.9,214.53,Managed eco-centric encoding,New Juan,0,Cameroon,2016-03-30 19:09:50,0 309 | 82.7,35,61383.79,231.07,Visionary multi-tasking alliance,Amyfurt,0,Somalia,2016-01-09 15:49:28,0 310 | 84.88,32,63924.82,186.48,Ameliorated tangible hierarchy,Harrishaven,0,Lebanon,2016-04-18 03:41:56,0 311 | 54.92,54,23975.35,161.16,Extended interactive model,Roberttown,0,Saint Pierre and Miquelon,2016-06-13 13:59:51,1 312 | 76.56,34,70179.11,221.53,Universal bi-directional extranet,Jeremyshire,1,Dominica,2016-04-23 08:15:31,0 313 | 69.74,49,66524.8,243.37,Enhanced maximized access,Birdshire,0,Hungary,2016-03-27 16:41:29,0 314 | 75.55,22,41851.38,169.4,Upgradable even-keeled challenge,New Amanda,0,Taiwan,2016-02-19 07:29:30,1 315 | 72.19,33,61275.18,250.35,Synchronized national infrastructure,Curtisview,1,Saint Lucia,2016-05-19 11:16:59,0 316 | 84.29,41,60638.38,232.54,Re-contextualized systemic time-frame,Jacksonmouth,0,Niue,2016-01-27 20:47:57,0 317 | 73.89,39,47160.53,110.68,Horizontal national architecture,North April,0,France,2016-04-20 00:41:53,1 318 | 75.84,21,48537.18,186.98,Reactive bi-directional workforce,Hayesmouth,0,Cyprus,2016-02-07 07:41:06,0 319 | 73.38,25,53058.91,236.19,Horizontal transitional challenge,South Corey,1,French Southern Territories,2016-04-21 09:30:35,0 320 | 80.72,31,68614.98,186.37,Re-engineered neutral success,Juliaport,0,Costa Rica,2016-04-19 05:15:28,0 321 | 62.06,44,44174.25,105.0,Adaptive contextually-based methodology,Port Paultown,0,Austria,2016-04-12 14:01:08,1 322 | 51.5,34,67050.16,135.31,Configurable dynamic adapter,East Vincentstad,0,Zambia,2016-03-15 11:25:48,1 323 | 90.97,37,54520.14,180.77,Multi-lateral empowering throughput,Kimberlytown,0,Congo,2016-02-16 18:21:36,0 324 | 86.78,30,54952.42,170.13,Fundamental zero tolerance solution,New Steve,1,United States of America,2016-02-18 23:08:59,0 325 | 66.18,35,69476.42,243.61,Proactive asymmetric definition,New Johnberg,0,Pitcairn Islands,2016-03-25 08:40:15,0 326 | 84.33,41,54989.93,240.95,Pre-emptive zero tolerance Local Area Network,Shawstad,0,Belize,2016-03-16 00:28:10,0 327 | 36.87,36,29398.61,195.91,Self-enabling incremental collaboration,New Rebecca,0,Anguilla,2016-01-28 11:50:40,1 328 | 34.78,48,42861.42,208.21,Exclusive even-keeled moratorium,Jeffreyburgh,1,South Africa,2016-03-24 02:01:55,1 329 | 76.84,32,65883.39,231.59,Reduced incremental productivity,Faithview,0,Singapore,2016-03-03 22:31:16,0 330 | 67.05,25,65421.39,220.92,Realigned scalable standardization,Richardsontown,0,Finland,2016-02-26 09:54:33,0 331 | 41.47,31,60953.93,219.79,Secured scalable Graphical User Interface,Port Brookeland,0,Martinique,2016-07-06 15:56:39,1 332 | 80.71,26,58476.57,200.58,Team-oriented context-sensitive installation,East Christopherbury,0,Cameroon,2016-06-24 05:50:22,0 333 | 80.09,31,66636.84,214.08,Pre-emptive systematic budgetary management,Port Christinemouth,0,Sweden,2016-05-23 21:00:45,0 334 | 56.3,49,67430.96,135.24,Fully-configurable high-level implementation,South Meghan,1,New Caledonia,2016-02-03 19:12:51,1 335 | 79.36,34,57260.41,245.78,Profound maximized workforce,Hessstad,1,Bosnia and Herzegovina,2016-04-28 22:54:37,0 336 | 86.38,40,66359.32,188.27,Cross-platform 4thgeneration focus group,Rhondaborough,1,Singapore,2016-03-19 14:57:00,0 337 | 38.94,41,57587.0,142.67,Optional mission-critical functionalities,Lewismouth,1,Falkland Islands (Malvinas),2016-07-15 09:08:42,1 338 | 87.26,35,63060.55,184.03,Multi-layered tangible portal,New Paul,0,Bosnia and Herzegovina,2016-05-12 04:35:59,0 339 | 75.32,28,59998.5,233.6,Reduced mobile structure,Lake Angela,1,Mauritius,2016-01-01 21:58:55,0 340 | 74.38,40,74024.61,220.05,Enhanced zero tolerance Graphic Interface,East Graceland,1,Indonesia,2016-03-13 13:50:25,0 341 | 65.9,22,60550.66,211.39,De-engineered tertiary secured line,Hartport,0,Czech Republic,2016-07-16 14:13:54,0 342 | 36.31,47,57983.3,168.92,Reverse-engineered well-modulated capability,East Yvonnechester,0,Eritrea,2016-04-18 00:49:33,1 343 | 72.23,48,52736.33,115.35,Integrated coherent pricing structure,Burgessside,0,Mexico,2016-07-17 01:13:56,1 344 | 88.12,38,46653.75,230.91,Realigned next generation projection,Hurleyborough,0,Gibraltar,2016-02-17 07:05:57,0 345 | 83.97,28,56986.73,205.5,Reactive needs-based instruction set,Garychester,1,Haiti,2016-06-16 02:33:22,0 346 | 61.09,26,55336.18,131.68,User-friendly well-modulated leverage,East Kevinbury,1,Falkland Islands (Malvinas),2016-04-09 16:31:15,1 347 | 65.77,21,42162.9,218.61,Function-based fault-tolerant model,Contrerasshire,1,Eritrea,2016-03-18 17:35:40,0 348 | 81.58,25,39699.13,199.39,Decentralized needs-based analyzer,Erikville,0,Hong Kong,2016-05-11 22:02:17,0 349 | 37.87,52,56394.82,188.56,Phased analyzing emulation,Robertsonburgh,1,Gambia,2016-05-25 20:10:02,1 350 | 76.2,37,75044.35,178.51,Multi-layered fresh-thinking process improvement,Karenton,0,Barbados,2016-02-29 19:26:35,0 351 | 60.91,19,53309.61,184.94,Upgradable directional system engine,Port Kathleenfort,0,Nauru,2016-06-09 14:24:06,1 352 | 74.49,28,58996.12,237.34,Persevering eco-centric flexibility,Lake Adrian,0,Peru,2016-01-30 16:15:29,0 353 | 73.71,23,56605.12,211.38,Inverse local hub,New Sheila,1,El Salvador,2016-02-15 05:35:54,0 354 | 78.19,30,62475.99,228.81,Triple-buffered needs-based Local Area Network,Mollyport,0,Libyan Arab Jamahiriya,2016-01-31 06:14:10,0 355 | 79.54,44,70492.6,217.68,Centralized multi-state hierarchy,Sandraland,1,Cambodia,2016-01-05 16:34:31,0 356 | 74.87,52,43698.53,126.97,Public-key non-volatile implementation,Charlenetown,0,Saint Barthelemy,2016-05-31 02:17:18,1 357 | 87.09,36,57737.51,221.98,Synergized coherent interface,Luischester,1,Reunion,2016-04-21 16:10:50,0 358 | 37.45,47,31281.01,167.86,Horizontal high-level concept,South Johnnymouth,0,Antigua and Barbuda,2016-04-10 03:30:16,1 359 | 49.84,39,45800.48,111.59,Reduced multimedia project,Hannaport,0,Samoa,2016-02-09 07:21:25,1 360 | 51.38,59,42362.49,158.56,Object-based modular functionalities,East Anthony,0,Afghanistan,2016-06-17 17:11:16,1 361 | 83.4,34,66691.23,207.87,Polarized multimedia system engine,West Daleborough,0,Azerbaijan,2016-05-22 21:54:23,0 362 | 38.91,33,56369.74,150.8,Versatile reciprocal structure,Morrismouth,1,Philippines,2016-07-13 07:41:42,1 363 | 62.14,41,59397.89,110.93,Upgradable multi-tasking initiative,North Andrewstad,1,Angola,2016-01-23 18:59:21,1 364 | 79.72,28,66025.11,193.8,Configurable tertiary budgetary management,Wrightburgh,1,Albania,2016-05-20 12:17:59,0 365 | 73.3,36,68211.35,135.72,Adaptive asynchronous attitude,West Tanya,1,Hungary,2016-01-30 04:38:41,1 366 | 69.11,42,73608.99,231.48,Face-to-face mission-critical definition,Novaktown,1,Faroe Islands,2016-04-21 12:34:28,0 367 | 71.9,54,61228.96,140.15,Inverse zero tolerance customer loyalty,Timothymouth,1,Czech Republic,2016-04-22 20:32:17,1 368 | 72.45,29,72325.91,195.36,Centralized 24hour synergy,Robertmouth,1,Svalbard & Jan Mayen Islands,2016-01-11 06:02:27,0 369 | 77.07,40,44559.43,261.02,Face-to-face analyzing encryption,Stephenborough,0,Afghanistan,2016-03-01 10:01:35,0 370 | 74.62,36,73207.15,217.79,Self-enabling even-keeled methodology,Lake Kurtmouth,0,Rwanda,2016-04-04 08:19:54,0 371 | 82.07,25,46722.07,205.38,Function-based optimizing extranet,Lauraburgh,1,Panama,2016-06-20 06:30:06,0 372 | 58.6,50,45400.5,113.7,Organic asynchronous hierarchy,Rogerburgh,0,Samoa,2016-01-28 07:10:29,1 373 | 36.08,45,41417.27,151.47,Automated client-driven orchestration,Davidside,1,United States Minor Outlying Islands,2016-07-03 04:11:40,1 374 | 79.44,26,60845.55,206.79,Public-key zero-defect analyzer,West Thomas,0,Greece,2016-05-15 13:18:34,0 375 | 41.73,47,60812.77,144.71,Proactive client-server productivity,Andersonchester,0,Cote d'Ivoire,2016-04-08 22:48:25,1 376 | 73.19,25,64267.88,203.74,Cloned incremental matrices,North Ronaldshire,1,Pakistan,2016-01-19 12:18:13,0 377 | 77.6,24,58151.87,197.33,Open-architected system-worthy task-force,Greghaven,1,Anguilla,2016-05-26 15:40:26,0 378 | 89.0,37,52079.18,222.26,Devolved regional moderator,Jordanmouth,1,Cyprus,2016-01-26 15:56:55,0 379 | 69.2,42,26023.99,123.8,Balanced value-added database,Meyersstad,0,Peru,2016-06-17 09:58:46,1 380 | 67.56,31,62318.38,125.45,Seamless composite budgetary management,Michelleside,0,Kenya,2016-04-25 21:15:39,1 381 | 81.11,39,56216.57,248.19,Total cohesive moratorium,South Robert,1,Chad,2016-07-13 11:41:29,0 382 | 80.22,30,61806.31,224.58,Integrated motivating neural-net,New Tyler,0,Kyrgyz Republic,2016-07-05 15:14:10,0 383 | 43.63,41,51662.24,123.25,Exclusive zero tolerance frame,Jordanshire,1,Albania,2016-03-15 14:06:17,1 384 | 77.66,29,67080.94,168.15,Operative scalable emulation,Reyesland,0,Gabon,2016-06-19 22:08:15,0 385 | 74.63,26,51975.41,235.99,Enhanced asymmetric installation,New Traceystad,1,Dominican Republic,2016-07-05 20:16:13,0 386 | 49.67,27,28019.09,153.69,Face-to-face reciprocal methodology,Port Brian,0,Zimbabwe,2016-05-09 08:44:55,1 387 | 80.59,37,67744.56,224.23,Robust responsive collaboration,Lake Courtney,0,Croatia,2016-07-21 23:14:35,0 388 | 83.49,33,66574.0,190.75,Polarized logistical hub,Samuelborough,1,Cambodia,2016-06-03 17:32:47,0 389 | 44.46,42,30487.48,132.66,Intuitive zero-defect framework,Christinehaven,1,Mongolia,2016-01-15 19:40:47,1 390 | 68.1,40,74903.41,227.73,Reactive composite project,Thomasstad,1,Honduras,2016-02-05 16:50:58,0 391 | 63.88,38,19991.72,136.85,Upgradable even-keeled hardware,Kristintown,0,Madagascar,2016-02-29 23:56:06,1 392 | 78.83,36,66050.63,234.64,Future-proofed responsive matrix,New Wanda,1,Qatar,2016-05-08 12:08:26,0 393 | 79.97,44,70449.04,216.0,Programmable empowering middleware,Mariebury,0,China,2016-07-13 01:48:46,0 394 | 80.51,28,64008.55,200.28,Robust dedicated system engine,Christopherville,1,Bangladesh,2016-01-08 02:34:06,0 395 | 62.26,26,70203.74,202.77,Public-key mission-critical core,New Jasmine,0,Swaziland,2016-06-08 12:25:49,0 396 | 66.99,47,27262.51,124.44,Operative actuating installation,Lopezberg,1,Tanzania,2016-06-15 11:56:41,1 397 | 71.05,20,49544.41,204.22,Self-enabling asynchronous knowledge user,Jenniferstad,1,Eritrea,2016-06-13 22:41:45,0 398 | 42.05,51,28357.27,174.55,Configurable 24/7 hub,West Eduardotown,1,Canada,2016-06-20 14:20:52,1 399 | 50.52,28,66929.03,219.69,Versatile responsive knowledge user,Davisfurt,0,Saint Kitts and Nevis,2016-04-03 06:17:22,1 400 | 76.24,40,75524.78,198.32,Managed impactful definition,Bakerhaven,1,Burkina Faso,2016-05-31 23:42:26,0 401 | 77.29,27,66265.34,201.24,Grass-roots 4thgeneration forecast,Paulshire,1,Tuvalu,2016-02-15 03:43:55,0 402 | 35.98,47,55993.68,165.52,Focused 3rdgeneration pricing structure,West Jane,1,El Salvador,2016-03-10 23:26:54,1 403 | 84.95,34,56379.3,230.36,Mandatory dedicated data-warehouse,Lake Brian,0,Madagascar,2016-02-26 17:01:01,0 404 | 39.34,43,31215.88,148.93,Proactive radical support,Alvaradoport,0,Bangladesh,2016-04-17 21:39:11,1 405 | 87.23,29,51015.11,202.12,Re-engineered responsive definition,Lake Kevin,0,American Samoa,2016-03-26 19:54:16,0 406 | 57.24,52,46473.14,117.35,Profound optimizing utilization,Richardsonland,1,Latvia,2016-06-29 21:39:42,1 407 | 81.58,41,55479.62,248.16,Cloned explicit middleware,East Sheriville,0,Moldova,2016-01-27 17:55:44,0 408 | 56.34,50,68713.7,139.02,Multi-channeled mission-critical success,Port Michealburgh,1,Anguilla,2016-03-17 23:39:28,1 409 | 48.73,27,34191.23,142.04,Versatile content-based protocol,Monicaview,0,Bangladesh,2016-07-09 16:23:33,1 410 | 51.68,49,51067.54,258.62,Seamless cohesive conglomeration,Katieport,0,Faroe Islands,2016-06-28 12:51:02,1 411 | 35.34,45,46693.76,152.86,De-engineered actuating hierarchy,East Brittanyville,0,Taiwan,2016-06-18 16:32:58,1 412 | 48.09,33,19345.36,180.42,Balanced motivating help-desk,West Travismouth,0,Heard Island and McDonald Islands,2016-05-28 12:38:37,1 413 | 78.68,29,66225.72,208.05,Inverse high-level capability,Leonchester,0,Israel,2016-01-16 16:40:30,0 414 | 68.82,20,38609.2,205.64,Cross-platform client-server hierarchy,Ramirezland,1,Bolivia,2016-07-11 15:45:23,0 415 | 56.99,40,37713.23,108.15,Sharable optimal capacity,Brownton,0,Bahamas,2016-07-16 23:08:54,1 416 | 86.63,39,63764.28,209.64,Face-to-face multimedia success,New Jessicaport,1,Costa Rica,2016-04-06 21:20:07,0 417 | 41.18,43,41866.55,129.25,Enterprise-wide incremental Internet solution,New Denisebury,1,Myanmar,2016-07-05 00:54:11,1 418 | 71.03,32,57846.68,120.85,Advanced systemic productivity,Keithtown,0,Netherlands Antilles,2016-02-17 23:47:00,1 419 | 72.92,29,69428.73,217.1,Customizable mission-critical adapter,Port Melissastad,1,Czech Republic,2016-03-15 17:33:15,0 420 | 77.14,24,60283.98,184.88,Horizontal heuristic synergy,Janiceview,1,Iceland,2016-01-21 18:51:01,0 421 | 60.7,43,79332.33,192.6,Multi-tiered multi-state moderator,Mataberg,1,Palau,2016-06-06 22:41:24,0 422 | 34.3,41,53167.68,160.74,Re-contextualized reciprocal interface,West Melaniefurt,1,Libyan Arab Jamahiriya,2016-05-16 14:50:22,1 423 | 83.71,45,64564.07,220.48,Organized demand-driven knowledgebase,Millerfort,1,Kazakhstan,2016-04-17 19:10:56,0 424 | 53.38,35,60803.37,120.06,Total local synergy,Alexanderview,1,French Guiana,2016-03-30 01:05:34,1 425 | 58.03,31,28387.42,129.33,User-friendly bandwidth-monitored attitude,South Jade,0,Tuvalu,2016-06-29 09:04:31,1 426 | 43.59,36,58849.77,132.31,Re-engineered context-sensitive knowledge user,Lake Susan,1,Congo,2016-05-26 13:43:05,1 427 | 60.07,42,65963.37,120.75,Total user-facing hierarchy,South Vincentchester,1,United Kingdom,2016-04-15 10:16:49,1 428 | 54.43,37,75180.2,154.74,Balanced contextually-based pricing structure,Williamsmouth,1,Luxembourg,2016-05-31 09:06:29,1 429 | 81.99,33,61270.14,230.9,Inverse bi-directional knowledge user,Taylorport,0,French Polynesia,2016-02-15 14:13:47,0 430 | 60.53,29,56759.48,123.28,Networked even-keeled workforce,Williamsport,0,Papua New Guinea,2016-05-09 10:21:48,1 431 | 84.69,31,46160.63,231.85,Right-sized transitional parallelism,Emilyfurt,1,Maldives,2016-07-07 23:32:38,0 432 | 88.72,32,43870.51,211.87,Customer-focused system-worthy superstructure,East John,1,Zambia,2016-01-03 17:10:05,0 433 | 88.89,35,50439.49,218.8,Balanced 4thgeneration success,East Deborahhaven,1,Cook Islands,2016-07-17 18:55:38,0 434 | 69.58,43,28028.74,255.07,Cross-group value-added success,Port Katelynview,0,Congo,2016-04-04 18:36:59,1 435 | 85.23,36,64238.71,212.92,Visionary client-driven installation,Paulhaven,1,Senegal,2016-02-27 12:34:19,0 436 | 83.55,39,65816.38,221.18,Switchable well-modulated infrastructure,Elizabethmouth,1,Myanmar,2016-06-08 20:13:27,0 437 | 56.66,42,72684.44,139.42,Upgradable asymmetric emulation,Lake Jesus,0,Dominican Republic,2016-02-20 10:52:51,1 438 | 56.39,27,38817.4,248.12,Configurable tertiary capability,North Tylerland,1,Bahrain,2016-03-23 21:06:51,0 439 | 76.24,27,63976.44,214.42,Monitored dynamic instruction set,Munozberg,0,Puerto Rico,2016-06-07 01:29:06,0 440 | 57.64,36,37212.54,110.25,Robust web-enabled attitude,North Maryland,1,Chile,2016-01-18 15:18:01,1 441 | 78.18,23,52691.79,167.67,Customer-focused full-range neural-net,West Barbara,0,Bolivia,2016-06-09 19:32:27,0 442 | 46.04,32,65499.93,147.92,Universal transitional Graphical User Interface,Andrewborough,0,Serbia,2016-05-30 20:07:59,1 443 | 79.4,35,63966.72,236.87,User-centric intangible contingency,New Gabriel,0,Malaysia,2016-04-01 09:21:14,0 444 | 36.44,39,52400.88,147.64,Configurable disintermediate throughput,Port Patrickton,1,Estonia,2016-05-31 06:21:02,1 445 | 53.14,38,49111.47,109.0,Automated web-enabled migration,West Julia,1,Greenland,2016-07-03 22:13:19,1 446 | 32.84,40,41232.89,171.72,Triple-buffered 3rdgeneration migration,New Keithburgh,0,Trinidad and Tobago,2016-03-10 01:36:19,1 447 | 73.72,32,52140.04,256.4,Universal contextually-based system engine,Richardsland,1,Thailand,2016-03-18 02:39:26,0 448 | 38.1,34,60641.09,214.38,Optional secondary access,North Aaronchester,1,Philippines,2016-05-30 18:08:19,1 449 | 73.93,44,74180.05,218.22,Quality-focused scalable utilization,Lake Matthewland,0,Niue,2016-02-20 00:06:20,0 450 | 51.87,50,51869.87,119.65,Team-oriented dynamic forecast,Kevinberg,0,Afghanistan,2016-03-10 22:28:52,1 451 | 77.69,22,48852.58,169.88,Horizontal heuristic support,Morganfort,1,Angola,2016-06-21 14:32:32,0 452 | 43.41,28,59144.02,160.73,Customer-focused zero-defect process improvement,Lovemouth,0,Egypt,2016-02-05 15:26:37,1 453 | 55.92,24,33951.63,145.08,Focused systemic benchmark,Taylorhaven,0,Fiji,2016-05-31 21:41:46,1 454 | 80.67,34,58909.36,239.76,Seamless impactful info-mediaries,Jamesville,0,Portugal,2016-01-01 02:52:10,0 455 | 83.42,25,49850.52,183.42,Advanced heuristic firmware,East Toddfort,1,Austria,2016-03-04 14:10:12,0 456 | 82.12,52,28679.93,201.15,Fully-configurable client-driven customer loyalty,East Dana,1,Germany,2016-02-03 10:40:27,1 457 | 66.17,33,69869.66,238.45,Cross-group neutral synergy,West Lucas,0,Panama,2016-01-20 00:26:15,0 458 | 43.01,35,48347.64,127.37,Organized 24/7 middleware,Butlerfort,0,United States of America,2016-06-11 09:37:52,1 459 | 80.05,25,45959.86,219.94,Networked stable open architecture,Lindaside,1,Christmas Island,2016-03-08 05:48:20,0 460 | 64.88,42,70005.51,129.8,Customizable systematic service-desk,West Chloeborough,1,Equatorial Guinea,2016-02-14 22:23:30,1 461 | 79.82,26,51512.66,223.28,Function-based directional productivity,Jayville,1,Micronesia,2016-07-17 22:04:54,0 462 | 48.03,40,25598.75,134.6,Networked stable array,East Lindsey,1,Malta,2016-06-02 22:16:08,1 463 | 32.99,45,49282.87,177.46,Phased full-range hardware,Masseyshire,0,Ecuador,2016-04-30 19:42:04,1 464 | 74.88,27,67240.25,175.17,Organized empowering policy,Sarahton,1,Sudan,2016-04-17 06:58:18,0 465 | 36.49,52,42136.33,196.61,Object-based system-worthy superstructure,Ryanhaven,1,Lao People's Democratic Republic,2016-03-09 00:41:46,1 466 | 88.04,45,62589.84,191.17,Profound explicit hardware,Lake Deborahburgh,1,Saint Vincent and the Grenadines,2016-03-07 20:02:51,0 467 | 45.7,33,67384.31,151.12,Self-enabling multimedia system engine,New Williammouth,1,Switzerland,2016-05-26 10:33:00,1 468 | 82.38,35,25603.93,159.6,Polarized analyzing intranet,Port Blake,0,Spain,2016-07-18 01:36:37,1 469 | 52.68,23,39616.0,149.2,Vision-oriented attitude-oriented Internet solution,West Richard,1,Turks and Caicos Islands,2016-07-16 05:56:42,1 470 | 65.59,47,28265.81,121.81,Digitized disintermediate ability,Brandymouth,0,Indonesia,2016-03-22 06:41:38,1 471 | 65.65,25,63879.72,224.92,Intuitive explicit firmware,Sandraville,1,Cook Islands,2016-06-03 06:34:44,0 472 | 43.84,36,70592.81,167.42,Public-key real-time definition,Port Jessica,0,Australia,2016-06-28 09:19:06,1 473 | 67.69,37,76408.19,216.57,Monitored content-based implementation,Lake Jasonchester,0,Finland,2016-07-18 18:33:05,0 474 | 78.37,24,55015.08,207.27,Quality-focused zero-defect budgetary management,Pearsonfort,0,Pakistan,2016-01-23 04:47:37,0 475 | 81.46,29,51636.12,231.54,Intuitive fresh-thinking moderator,Sellerstown,0,Ireland,2016-02-29 11:00:06,0 476 | 47.48,31,29359.2,141.34,Reverse-engineered 24hour hardware,Yuton,0,Eritrea,2016-06-30 00:19:33,1 477 | 75.15,33,71296.67,219.49,Synchronized zero tolerance product,Smithtown,1,France,2016-06-19 18:19:38,0 478 | 78.76,24,46422.76,219.98,Reactive interactive protocol,Joanntown,1,Austria,2016-01-08 08:08:47,0 479 | 44.96,50,52802.0,132.71,Focused fresh-thinking Graphic Interface,South Peter,1,Heard Island and McDonald Islands,2016-01-02 12:25:36,1 480 | 39.56,41,59243.46,143.13,Ameliorated exuding solution,Port Mitchell,1,Western Sahara,2016-05-13 11:57:12,1 481 | 39.76,28,35350.55,196.83,Integrated maximized service-desk,Pottermouth,1,Liberia,2016-02-08 14:02:22,1 482 | 57.11,22,59677.64,207.17,Self-enabling tertiary challenge,Lake Jonathanview,1,Dominican Republic,2016-06-07 23:46:51,0 483 | 83.26,40,70225.6,187.76,Decentralized foreground infrastructure,Alanview,1,Tonga,2016-01-02 14:36:03,0 484 | 69.42,25,65791.17,213.38,Quality-focused hybrid frame,Carterport,0,Lao People's Democratic Republic,2016-02-13 04:16:08,0 485 | 50.6,30,34191.13,129.88,Realigned reciprocal framework,New Daniellefort,1,United States of America,2016-05-03 12:57:19,1 486 | 46.2,37,51315.38,119.3,Distributed maximized ability,Welchshire,0,Belgium,2016-04-03 11:38:36,1 487 | 66.88,35,62790.96,119.47,Polarized bifurcated array,Russellville,1,Indonesia,2016-03-23 19:58:15,1 488 | 83.97,40,66291.67,158.42,Progressive asynchronous adapter,West Lisa,1,Croatia,2016-02-02 11:49:18,0 489 | 76.56,30,68030.18,213.75,Business-focused high-level hardware,Greentown,0,Brunei Darussalam,2016-03-08 10:39:16,0 490 | 35.49,48,43974.49,159.77,Fully-configurable holistic throughput,Timothyport,0,American Samoa,2016-04-08 14:35:44,1 491 | 80.29,31,49457.48,244.87,Ameliorated contextually-based collaboration,Teresahaven,1,Netherlands Antilles,2016-06-30 00:40:31,0 492 | 50.19,40,33987.27,117.3,Progressive uniform budgetary management,Lake Stephenborough,0,Thailand,2016-03-25 19:02:35,1 493 | 59.12,33,28210.03,124.54,Synergistic stable infrastructure,Silvaton,0,Greece,2016-05-12 21:32:06,1 494 | 59.88,30,75535.14,193.63,Reverse-engineered content-based intranet,West Michaelstad,1,French Polynesia,2016-03-02 05:11:01,0 495 | 59.7,28,49158.5,120.25,Expanded zero administration attitude,Florestown,0,Guernsey,2016-05-10 14:12:31,1 496 | 67.8,30,39809.69,117.75,Team-oriented 6thgeneration extranet,New Jay,1,Isle of Man,2016-03-03 02:59:37,1 497 | 81.59,35,65826.53,223.16,Managed disintermediate capability,North Lisachester,0,Holy See (Vatican City State),2016-07-04 11:03:49,0 498 | 81.1,29,61172.07,216.49,Front-line dynamic model,Port Stacy,1,El Salvador,2016-07-08 03:47:41,0 499 | 41.7,39,42898.21,126.95,Innovative regional structure,Jensenton,0,China,2016-05-27 05:35:27,1 500 | 73.94,27,68333.01,173.49,Function-based incremental standardization,North Alexandra,0,Myanmar,2016-02-10 13:46:35,0 501 | 58.35,37,70232.95,132.63,Universal asymmetric workforce,Rivasland,0,Macao,2016-06-12 21:21:53,1 502 | 51.56,46,63102.19,124.85,Business-focused client-driven forecast,Helenborough,0,Australia,2016-01-07 13:58:51,1 503 | 79.81,37,51847.26,253.17,Realigned global initiative,Garnerberg,0,United States Virgin Islands,2016-05-13 14:12:39,0 504 | 66.17,26,63580.22,228.7,Business-focused maximized complexity,North Anaport,0,Mexico,2016-05-02 00:01:56,0 505 | 58.21,37,47575.44,105.94,Open-source global strategy,Pattymouth,0,Djibouti,2016-02-07 17:06:35,1 506 | 66.12,49,39031.89,113.8,Stand-alone motivating moratorium,South Alexisborough,0,Cote d'Ivoire,2016-02-15 07:27:41,1 507 | 80.47,42,70505.06,215.18,Grass-roots multimedia policy,East Jennifer,1,Mali,2016-02-21 05:23:28,0 508 | 77.05,31,62161.26,236.64,Upgradable local migration,Hallfort,0,Jamaica,2016-03-20 22:27:25,0 509 | 49.99,41,61068.26,121.07,Profound bottom-line standardization,New Charleschester,0,Romania,2016-03-24 09:34:00,1 510 | 80.3,58,49090.51,173.43,Managed client-server access,East Breannafurt,0,Cayman Islands,2016-04-04 20:01:12,1 511 | 79.36,33,62330.75,234.72,Cross-platform directional intranet,East Susanland,1,Gambia,2016-01-02 04:50:44,0 512 | 57.86,30,18819.34,166.86,Horizontal modular success,Estesfurt,0,Algeria,2016-07-08 17:14:01,1 513 | 70.29,26,62053.37,231.37,Vision-oriented multi-tasking success,Shirleyfort,1,Puerto Rico,2016-03-28 19:48:37,0 514 | 84.53,33,61922.06,215.18,Optional multi-state hardware,Douglasview,1,Norfolk Island,2016-07-11 09:32:53,0 515 | 59.13,44,49525.37,106.04,Upgradable heuristic system engine,South Lisa,1,Turkey,2016-06-09 17:11:02,1 516 | 81.51,41,53412.32,250.03,Future-proofed modular utilization,Kingshire,0,Guinea,2016-05-19 09:30:12,0 517 | 42.94,37,56681.65,130.4,Synergistic dynamic orchestration,Rebeccamouth,1,Moldova,2016-04-12 12:35:39,1 518 | 84.81,32,43299.63,233.93,Multi-layered stable encoding,Brownbury,1,Greece,2016-07-04 23:17:47,0 519 | 82.79,34,47997.75,132.08,Team-oriented zero-defect initiative,South Aaron,0,American Samoa,2016-02-01 00:52:29,1 520 | 59.22,55,39131.53,126.39,Polarized 5thgeneration matrix,North Andrew,1,Honduras,2016-01-13 02:39:00,1 521 | 35.0,40,46033.73,151.25,Fully-configurable context-sensitive Graphic Interface,South Walter,1,Mongolia,2016-06-18 16:02:34,1 522 | 46.61,42,65856.74,136.18,Progressive intermediate throughput,Catherinefort,0,Ethiopia,2016-01-01 20:17:49,1 523 | 63.26,29,54787.37,120.46,Customizable holistic archive,East Donna,1,Ethiopia,2016-03-02 04:02:45,1 524 | 79.16,32,69562.46,202.9,Compatible intermediate concept,East Timothy,1,Sri Lanka,2016-03-30 20:23:48,0 525 | 67.94,43,68447.17,128.16,Assimilated next generation firmware,North Kimberly,0,Morocco,2016-05-01 00:23:13,1 526 | 79.91,32,62772.42,230.18,Total zero administration software,South Stephanieport,1,United Arab Emirates,2016-06-17 03:02:55,0 527 | 66.14,41,78092.95,165.27,Re-engineered impactful software,North Isabellaville,0,Western Sahara,2016-03-23 08:52:31,0 528 | 43.65,39,63649.04,138.87,Business-focused background synergy,North Aaronburgh,0,Western Sahara,2016-05-08 22:24:27,1 529 | 59.61,21,60637.62,198.45,Future-proofed coherent budgetary management,Port James,1,Cambodia,2016-04-06 05:55:43,0 530 | 46.61,52,27241.11,156.99,Ergonomic methodical encoding,Danielview,0,New Zealand,2016-04-05 05:54:15,1 531 | 89.37,34,42760.22,162.03,Compatible dedicated productivity,Port Stacey,1,Australia,2016-04-16 12:26:31,0 532 | 65.1,49,59457.52,118.1,Up-sized real-time methodology,West Kevinfurt,1,Bulgaria,2016-06-01 03:44:42,1 533 | 53.44,42,42907.89,108.17,Up-sized next generation architecture,Lake Jennifer,1,Libyan Arab Jamahiriya,2016-04-04 22:00:15,1 534 | 79.53,51,46132.18,244.91,Managed 6thgeneration hierarchy,Reyesfurt,0,Barbados,2016-06-26 04:22:26,0 535 | 91.43,39,46964.11,209.91,Organic motivating model,West Carmenfurt,1,French Polynesia,2016-07-07 03:55:01,0 536 | 73.57,30,70377.23,212.38,Pre-emptive transitional protocol,North Stephanieberg,0,Uruguay,2016-03-20 08:22:50,0 537 | 78.76,32,70012.83,208.02,Managed attitude-oriented Internet solution,East Valerie,1,Uruguay,2016-04-20 10:04:29,0 538 | 76.49,23,56457.01,181.11,Public-key asynchronous matrix,Sherrishire,0,Brazil,2016-03-25 05:05:27,0 539 | 61.72,26,67279.06,218.49,Grass-roots systematic hardware,Port Daniel,0,Venezuela,2016-02-14 07:15:37,0 540 | 84.53,35,54773.99,236.29,User-centric composite contingency,Brownview,0,Myanmar,2016-03-26 00:32:02,0 541 | 72.03,34,70783.94,230.95,Up-sized bi-directional infrastructure,Greerton,1,Malta,2016-07-05 22:33:48,0 542 | 77.47,36,70510.59,222.91,Assimilated actuating policy,Hatfieldshire,1,Jamaica,2016-03-14 03:29:12,0 543 | 75.65,39,64021.55,247.9,Organized upward-trending contingency,Brianabury,1,Bahrain,2016-05-30 02:34:25,0 544 | 78.15,33,72042.85,194.37,Ergonomic neutral portal,New Maria,0,Algeria,2016-03-07 22:32:15,0 545 | 63.8,38,36037.33,108.7,Adaptive demand-driven knowledgebase,Colebury,1,Tuvalu,2016-03-19 00:27:58,1 546 | 76.59,29,67526.92,211.64,Reverse-engineered maximized focus group,Calebberg,0,Georgia,2016-06-18 05:17:33,0 547 | 42.6,55,55121.65,168.29,Switchable analyzing encryption,Lake Ian,0,Cambodia,2016-07-11 18:12:43,1 548 | 78.77,28,63497.62,211.83,Public-key intangible Graphical User Interface,Gomezport,0,Guam,2016-01-01 08:27:06,0 549 | 83.4,39,60879.48,235.01,Advanced local task-force,Shaneland,0,Tanzania,2016-04-07 01:57:38,0 550 | 79.53,33,61467.33,236.72,Profound well-modulated array,East Aaron,0,Indonesia,2016-02-28 22:02:14,0 551 | 73.89,35,70495.64,229.99,Multi-channeled asymmetric installation,Dustinborough,1,Somalia,2016-06-26 17:25:55,0 552 | 75.8,36,71222.4,224.9,Multi-layered fresh-thinking neural-net,East Michaelland,0,Belize,2016-01-21 04:30:43,0 553 | 81.95,31,64698.58,208.76,Distributed cohesive migration,East Connie,1,Serbia,2016-05-01 21:46:37,0 554 | 56.39,58,32252.38,154.23,Programmable uniform website,West Shannon,0,Australia,2016-02-14 10:06:49,1 555 | 44.73,35,55316.97,127.56,Object-based neutral policy,North Lauraland,1,Guam,2016-01-27 18:25:42,1 556 | 38.35,33,47447.89,145.48,Horizontal global leverage,Port Christopher,1,Christmas Island,2016-06-16 20:24:33,1 557 | 72.53,37,73474.82,223.93,Synchronized grid-enabled moratorium,South Patrickfort,0,Papua New Guinea,2016-07-21 10:01:50,0 558 | 56.2,49,53549.94,114.85,Adaptive uniform capability,East Georgeside,1,Bahamas,2016-04-21 18:31:27,1 559 | 79.67,28,58576.12,226.79,Total grid-enabled application,Charlesbury,0,Comoros,2016-07-20 01:56:33,0 560 | 75.42,26,63373.7,164.25,Optional regional throughput,Millertown,1,Western Sahara,2016-02-26 17:14:14,0 561 | 78.64,31,60283.47,235.28,Integrated client-server definition,South Renee,1,Nicaragua,2016-01-16 17:56:05,0 562 | 67.69,44,37345.34,109.22,Fundamental methodical support,South Jackieberg,0,Guam,2016-04-01 01:57:12,1 563 | 38.35,41,34886.01,144.69,Synergistic reciprocal attitude,Loriville,1,Vanuatu,2016-06-24 08:42:20,1 564 | 59.52,44,67511.86,251.08,Managed 5thgeneration time-frame,Amandaland,1,Bolivia,2016-05-27 18:45:35,0 565 | 62.26,37,77988.71,166.19,Vision-oriented uniform knowledgebase,West Robertside,0,Malawi,2016-05-26 15:40:12,0 566 | 64.75,36,63001.03,117.66,Multi-tiered stable leverage,North Sarashire,0,Venezuela,2016-04-06 01:19:08,1 567 | 79.97,26,61747.98,185.45,Down-sized explicit budgetary management,Port Maria,1,Nepal,2016-01-08 19:38:45,0 568 | 47.9,42,48467.68,114.53,Cross-group human-resource time-frame,East Jessefort,0,United Kingdom,2016-02-24 19:08:11,1 569 | 80.38,30,55130.96,238.06,Business-focused holistic benchmark,Port Anthony,0,Albania,2016-03-10 07:07:31,0 570 | 64.51,42,79484.8,190.71,Virtual 5thgeneration neural-net,Edwardmouth,1,Madagascar,2016-04-29 07:49:01,0 571 | 71.28,37,67307.43,246.72,Distributed scalable orchestration,Dustinchester,1,Guyana,2016-04-10 16:08:09,0 572 | 50.32,40,27964.6,125.65,Realigned intangible benchmark,Rochabury,0,Yemen,2016-04-27 18:25:30,1 573 | 72.76,33,66431.87,240.63,Virtual impactful algorithm,Williamsport,1,India,2016-05-10 04:28:55,0 574 | 72.8,35,63551.67,249.54,Public-key solution-oriented focus group,Austinland,0,Puerto Rico,2016-01-03 23:21:26,0 575 | 74.59,23,40135.06,158.35,Phased clear-thinking encoding,Lake Gerald,1,United States Virgin Islands,2016-02-15 16:52:04,1 576 | 46.66,45,49101.67,118.16,Grass-roots mission-critical emulation,Wrightview,0,Antigua and Barbuda,2016-03-09 02:07:17,1 577 | 48.86,54,53188.69,134.46,Proactive encompassing paradigm,Perryburgh,0,French Guiana,2016-01-09 17:33:03,1 578 | 37.05,39,49742.83,142.81,Automated object-oriented firmware,Tracyhaven,1,Antigua and Barbuda,2016-02-03 05:47:09,1 579 | 81.21,36,63394.41,233.04,User-friendly content-based customer loyalty,South Jaimeview,0,Turkmenistan,2016-01-02 09:30:11,0 580 | 66.89,23,64433.99,208.24,Universal incremental array,Sandersland,1,Honduras,2016-01-04 07:28:43,0 581 | 68.11,38,73884.48,231.21,Reactive national success,South Meredithmouth,0,Seychelles,2016-01-07 21:21:50,0 582 | 69.15,46,36424.94,112.72,Automated multi-state toolset,Richardsonshire,0,Cyprus,2016-07-24 00:22:16,1 583 | 65.72,36,28275.48,120.12,Managed didactic flexibility,Kimberlymouth,0,Saint Pierre and Miquelon,2016-02-13 13:57:53,1 584 | 40.04,27,48098.86,161.58,Cross-platform neutral system engine,Meghanchester,0,Poland,2016-05-08 10:25:08,1 585 | 68.6,33,68448.94,135.08,Focused high-level frame,Tammyshire,0,Taiwan,2016-02-17 18:50:57,1 586 | 56.16,25,66429.84,164.25,Seamless motivating approach,Millerbury,1,Cote d'Ivoire,2016-01-22 19:43:53,1 587 | 78.6,46,41768.13,254.59,Enhanced systematic adapter,Lake Elizabethside,1,Micronesia,2016-07-20 13:21:37,0 588 | 78.29,38,57844.96,252.07,Networked regional Local Area Network,Villanuevaton,0,Liberia,2016-01-05 20:58:42,0 589 | 43.83,45,35684.82,129.01,Total human-resource flexibility,Greerport,0,Saudi Arabia,2016-01-29 05:39:16,1 590 | 77.31,32,62792.43,238.1,Assimilated homogeneous service-desk,North Garyhaven,0,Nepal,2016-06-17 20:18:27,0 591 | 39.86,28,51171.23,161.24,Ergonomic zero tolerance encoding,East Sharon,0,Ghana,2016-02-23 13:55:48,1 592 | 66.77,25,58847.07,141.13,Cross-platform zero-defect structure,Johnstonmouth,0,Iran,2016-07-09 11:18:02,1 593 | 57.2,42,57739.03,110.66,Innovative maximized groupware,East Heatherside,0,New Zealand,2016-03-19 11:09:36,1 594 | 73.15,25,64631.22,211.12,Face-to-face executive encryption,Lake Patrick,1,Libyan Arab Jamahiriya,2016-01-29 07:14:04,0 595 | 82.07,24,50337.93,193.97,Monitored local Internet solution,Richardsonmouth,0,Sri Lanka,2016-06-14 07:02:09,0 596 | 49.84,38,67781.31,135.24,Phased hybrid superstructure,Jenniferhaven,1,United Arab Emirates,2016-05-18 03:19:03,1 597 | 43.97,36,68863.95,156.97,User-friendly grid-enabled analyzer,Boyerberg,1,Indonesia,2016-01-30 09:54:03,1 598 | 77.25,27,55901.12,231.38,Pre-emptive neutral contingency,Port Elijah,1,Saint Vincent and the Grenadines,2016-04-25 16:58:50,0 599 | 74.84,37,64775.1,246.44,User-friendly impactful time-frame,Knappburgh,1,Mongolia,2016-01-14 16:30:38,0 600 | 83.53,36,67686.16,204.56,Customizable methodical Graphical User Interface,New Dawnland,0,Honduras,2016-07-06 05:34:52,0 601 | 38.63,48,57777.11,222.11,Cross-platform logistical pricing structure,Chapmanmouth,0,Papua New Guinea,2016-04-07 10:51:05,1 602 | 84.0,48,46868.53,136.21,Inverse discrete extranet,Robertside,1,Kyrgyz Republic,2016-04-17 05:08:52,1 603 | 52.13,50,40926.93,118.27,Open-source even-keeled database,West Raymondmouth,1,Ethiopia,2016-01-28 17:03:54,1 604 | 71.83,40,22205.74,135.48,Diverse background ability,Costaburgh,1,Rwanda,2016-02-18 22:42:33,1 605 | 78.36,24,58920.44,196.77,Multi-tiered foreground Graphic Interface,Kristineberg,1,Kyrgyz Republic,2016-06-24 21:09:58,0 606 | 50.18,35,63006.14,127.82,Customizable hybrid system engine,Sandrashire,1,Grenada,2016-06-20 04:24:41,1 607 | 64.67,51,24316.61,138.35,Horizontal incremental website,Andersonfurt,1,Togo,2016-02-14 16:33:29,1 608 | 69.5,26,68348.99,203.84,Front-line systemic capability,Tranland,0,Pakistan,2016-02-27 13:51:44,0 609 | 65.22,30,66263.37,240.09,Fully-configurable foreground solution,Michaelland,1,Falkland Islands (Malvinas),2016-05-07 15:16:07,0 610 | 62.06,40,63493.6,116.27,Digitized radical array,East Rachaelfurt,1,Jersey,2016-03-16 20:10:53,1 611 | 84.29,30,56984.09,160.33,Team-oriented transitional methodology,Lake Johnbury,1,Cayman Islands,2016-06-26 02:06:59,1 612 | 32.91,37,51691.55,181.02,Future-proofed fresh-thinking conglomeration,Elizabethstad,0,South Africa,2016-07-17 14:26:04,1 613 | 39.5,31,49911.25,148.19,Operative multi-tasking Graphic Interface,West Brad,1,Micronesia,2016-01-28 16:42:36,1 614 | 75.19,31,33502.57,245.76,Implemented discrete frame,Johnstonshire,1,Tajikistan,2016-06-16 18:04:51,0 615 | 76.21,31,65834.97,228.94,Ameliorated exuding encryption,Lake Timothy,1,Bolivia,2016-06-19 23:21:38,0 616 | 67.76,31,66176.97,242.59,Programmable high-level benchmark,Anthonyfurt,0,Cameroon,2016-05-24 17:42:58,0 617 | 40.01,53,51463.17,161.77,Sharable multimedia conglomeration,East Brettton,0,Ecuador,2016-03-01 22:06:37,1 618 | 52.7,41,41059.64,109.34,Team-oriented high-level orchestration,New Matthew,1,Zambia,2016-01-31 08:50:38,1 619 | 68.41,38,61428.18,259.76,Grass-roots empowering paradigm,Christopherchester,0,Guinea-Bissau,2016-04-30 15:27:22,0 620 | 35.55,39,51593.46,151.18,Robust object-oriented Graphic Interface,Westshire,0,Micronesia,2016-01-13 20:38:35,1 621 | 74.54,24,57518.73,219.75,Switchable secondary ability,Alexisland,0,Bahamas,2016-03-30 16:15:59,0 622 | 81.75,24,52656.13,190.08,Open-architected web-enabled benchmark,Kevinchester,1,Cape Verde,2016-04-29 18:53:43,0 623 | 87.85,31,52178.98,210.27,Compatible scalable emulation,New Patriciashire,1,French Polynesia,2016-06-14 19:48:34,0 624 | 60.23,60,46239.14,151.54,Seamless optimal contingency,Port Brenda,1,Saudi Arabia,2016-07-15 15:43:36,1 625 | 87.97,35,48918.55,149.25,Secured secondary superstructure,Port Brianfort,1,France,2016-03-24 05:38:01,0 626 | 78.17,27,65227.79,192.27,Automated mobile model,Portermouth,1,Burundi,2016-04-26 20:57:48,0 627 | 67.91,23,55002.05,146.8,Re-engineered non-volatile neural-net,Hubbardmouth,1,Latvia,2016-01-12 03:28:31,1 628 | 85.77,27,52261.73,191.78,Implemented disintermediate attitude,South Brian,1,Morocco,2016-04-09 23:26:42,0 629 | 41.16,49,59448.44,150.83,Configurable interactive contingency,Hendrixmouth,1,Venezuela,2016-03-28 09:15:58,1 630 | 53.54,39,47314.45,108.03,Optimized systemic capability,Julietown,0,Palau,2016-06-23 11:05:01,1 631 | 73.94,26,55411.06,236.15,Front-line non-volatile implementation,Lukeport,1,Isle of Man,2016-01-24 01:53:14,0 632 | 63.43,29,66504.16,236.75,Ergonomic 24/7 solution,New Shane,1,Peru,2016-04-15 10:18:55,0 633 | 84.59,36,47169.14,241.8,Integrated grid-enabled budgetary management,Lake Jillville,1,Belgium,2016-04-26 13:13:20,0 634 | 70.13,31,70889.68,224.98,Profit-focused systemic support,Johnsonfort,0,Croatia,2016-05-16 23:21:06,0 635 | 40.19,37,55358.88,136.99,Right-sized system-worthy project,Adamsbury,0,France,2016-01-18 02:51:13,1 636 | 58.95,55,56242.7,131.29,Proactive actuating Graphical User Interface,East Maureen,1,Slovenia,2016-06-20 08:34:46,1 637 | 35.76,51,45522.44,195.07,Versatile optimizing projection,North Angelastad,0,Peru,2016-07-18 04:53:22,1 638 | 59.36,49,46931.03,110.84,Universal multi-state system engine,Amandafort,0,Belarus,2016-07-01 01:12:04,1 639 | 91.1,40,55499.69,198.13,Secured intermediate approach,Michaelmouth,1,Bolivia,2016-03-07 22:51:00,0 640 | 61.04,41,75805.12,149.21,Operative didactic Local Area Network,Ronaldport,0,Benin,2016-05-02 15:31:28,1 641 | 74.06,23,40345.49,225.99,Phased content-based middleware,Port Davidland,0,Wallis and Futuna,2016-07-23 06:18:51,0 642 | 64.63,45,15598.29,158.8,Triple-buffered high-level Internet solution,Isaacborough,1,Azerbaijan,2016-06-12 03:11:04,1 643 | 81.29,28,33239.2,219.72,Synergized well-modulated Graphical User Interface,Lake Michael,0,Mongolia,2016-02-15 20:41:05,0 644 | 76.07,36,68033.54,235.56,Implemented bottom-line implementation,West Michaelshire,0,Denmark,2016-01-23 01:42:28,0 645 | 75.92,22,38427.66,182.65,Monitored context-sensitive initiative,Port Calvintown,0,Russian Federation,2016-02-26 01:18:44,0 646 | 78.35,46,53185.34,253.48,Pre-emptive client-server open system,Parkerhaven,0,Brazil,2016-01-11 02:07:14,0 647 | 46.14,28,39723.97,137.97,Seamless bandwidth-monitored knowledge user,Markhaven,1,Ethiopia,2016-04-04 13:56:14,1 648 | 44.33,41,43386.07,120.63,Ergonomic empowering frame,Estradashire,0,Guyana,2016-01-14 09:27:59,1 649 | 46.43,28,53922.43,137.2,Reverse-engineered background Graphic Interface,Brianland,1,Ethiopia,2016-04-25 03:18:45,1 650 | 66.04,27,71881.84,199.76,Synergistic non-volatile analyzer,Cassandratown,0,Mauritius,2016-03-05 23:02:11,0 651 | 84.31,29,47139.21,225.87,Object-based optimal solution,West Dannyberg,0,Djibouti,2016-01-06 21:43:22,0 652 | 83.66,38,68877.02,175.14,Profound dynamic attitude,East Debraborough,0,Syrian Arab Republic,2016-02-18 03:58:36,0 653 | 81.25,33,65186.58,222.35,Enhanced system-worthy toolset,Frankchester,1,Saint Martin,2016-04-16 14:15:55,0 654 | 85.26,32,55424.24,224.07,Reverse-engineered dynamic function,Lisafort,1,Netherlands Antilles,2016-02-24 06:18:11,0 655 | 86.53,46,46500.11,233.36,Networked responsive application,Colemanshire,0,Greece,2016-06-29 01:19:21,0 656 | 76.44,26,58820.16,224.2,Distributed intangible database,Troyville,1,Madagascar,2016-01-05 06:34:20,0 657 | 52.84,43,28495.21,122.31,Multi-tiered mobile encoding,Hobbsbury,0,Senegal,2016-07-16 10:14:04,1 658 | 85.24,31,61840.26,182.84,Optional contextually-based flexibility,Harrisonmouth,1,Burkina Faso,2016-06-17 03:23:13,0 659 | 74.71,46,37908.29,258.06,Proactive local focus group,Port Eugeneport,1,Czech Republic,2016-06-13 11:06:40,0 660 | 82.95,39,69805.7,201.29,Customer-focused impactful success,Karenmouth,0,Lao People's Democratic Republic,2016-04-05 08:18:45,0 661 | 76.42,26,60315.19,223.16,Open-source optimizing parallelism,Brendaburgh,1,Netherlands Antilles,2016-04-17 18:38:14,0 662 | 42.04,49,67323.0,182.11,Organic logistical adapter,New Christinatown,0,Qatar,2016-02-03 16:54:33,1 663 | 46.28,26,50055.33,228.78,Stand-alone eco-centric system engine,Jacksonstad,1,Andorra,2016-04-18 21:07:28,1 664 | 48.26,50,43573.66,122.45,User-centric intermediate knowledge user,South Margaret,1,Liechtenstein,2016-06-18 22:31:22,1 665 | 71.03,55,28186.65,150.77,Programmable didactic capacity,Port Georgebury,0,China,2016-03-12 07:18:36,1 666 | 81.37,33,66412.04,215.04,Enhanced regional conglomeration,New Jessicaport,0,Vietnam,2016-01-15 01:20:05,0 667 | 58.05,32,15879.1,195.54,Total asynchronous architecture,Sanderstown,1,Tajikistan,2016-02-12 10:39:10,1 668 | 75.0,29,63965.16,230.36,Secured upward-trending benchmark,Perezland,1,Eritrea,2016-02-16 02:29:03,0 669 | 79.61,31,58342.63,235.97,Customizable value-added project,Luisfurt,0,Monaco,2016-04-04 21:23:13,0 670 | 52.56,31,33147.19,250.36,Integrated interactive support,New Karenberg,1,Israel,2016-04-24 01:48:21,1 671 | 62.18,33,65899.68,126.44,Reactive impactful challenge,West Leahton,0,Hungary,2016-05-20 00:00:48,1 672 | 77.89,26,64188.5,201.54,Switchable multi-state success,West Sharon,0,Singapore,2016-05-15 03:10:50,0 673 | 66.08,61,58966.22,184.23,Synchronized multi-tasking ability,Klineside,1,Cuba,2016-01-07 23:02:43,1 674 | 89.21,33,44078.24,210.53,Fundamental clear-thinking knowledgebase,Lake Cynthia,0,Reunion,2016-07-19 12:05:58,0 675 | 49.96,55,60968.62,151.94,Multi-layered user-facing parallelism,South Cynthiashire,1,Zambia,2016-04-04 00:02:20,1 676 | 77.44,28,65620.25,210.39,Front-line incremental access,Lake Jacob,0,Gabon,2016-06-10 04:21:57,0 677 | 82.58,38,65496.78,225.23,Open-architected zero administration secured line,West Samantha,1,Dominica,2016-03-11 14:50:56,0 678 | 39.36,29,52462.04,161.79,Mandatory disintermediate info-mediaries,Jeremybury,1,Bahamas,2016-01-14 20:58:10,1 679 | 47.23,38,70582.55,149.8,Implemented context-sensitive Local Area Network,Blevinstown,1,Tokelau,2016-06-22 05:22:58,1 680 | 87.85,34,51816.27,153.01,Digitized interactive initiative,Meyerchester,0,Turkmenistan,2016-03-19 08:00:58,0 681 | 65.57,46,23410.75,130.86,Implemented asynchronous application,Reginamouth,0,Belgium,2016-04-15 15:07:17,1 682 | 78.01,26,62729.4,200.71,Focused multi-state workforce,Donaldshire,1,French Guiana,2016-03-28 02:29:19,0 683 | 44.15,28,48867.67,141.96,Proactive secondary monitoring,Salazarbury,1,Martinique,2016-01-22 15:03:25,1 684 | 43.57,36,50971.73,125.2,Front-line upward-trending groupware,Lake Joshuafurt,1,French Polynesia,2016-06-25 17:33:35,1 685 | 76.83,28,67990.84,192.81,Quality-focused 5thgeneration orchestration,Wintersfort,0,Ecuador,2016-03-04 14:33:38,0 686 | 42.06,34,43241.19,131.55,Multi-layered secondary software,Jamesmouth,0,Puerto Rico,2016-06-29 02:48:44,1 687 | 76.27,27,60082.66,226.69,Total coherent superstructure,Laurieside,1,United Arab Emirates,2016-06-18 01:42:37,0 688 | 74.27,37,65180.97,247.05,Monitored executive architecture,Andrewmouth,1,Burkina Faso,2016-01-31 09:57:34,0 689 | 73.27,28,67301.39,216.24,Front-line multi-state hub,West Angela,1,Luxembourg,2016-05-22 15:17:25,0 690 | 74.58,36,70701.31,230.52,Configurable mission-critical algorithm,East Carlos,0,Jamaica,2016-07-22 11:05:10,0 691 | 77.5,28,60997.84,225.34,Face-to-face responsive alliance,Kennedyfurt,1,Antarctica (the territory South of 60 deg S),2016-07-13 14:05:22,0 692 | 87.16,33,60805.93,197.15,Reduced holistic help-desk,Blairville,0,China,2016-02-11 11:50:26,0 693 | 87.16,37,50711.68,231.95,Pre-emptive content-based frame,East Donnatown,1,Western Sahara,2016-03-16 20:33:10,0 694 | 66.26,47,14548.06,179.04,Optional full-range projection,Matthewtown,1,Lebanon,2016-04-25 19:31:39,1 695 | 65.15,29,41335.84,117.3,Expanded value-added emulation,Brandonbury,0,Hong Kong,2016-07-14 22:43:29,1 696 | 68.25,33,76480.16,198.86,Organic well-modulated database,New Jamestown,1,Vanuatu,2016-05-30 08:02:35,0 697 | 73.49,38,67132.46,244.23,Organic 3rdgeneration encryption,Mosleyburgh,0,Vanuatu,2016-02-14 11:36:08,0 698 | 39.19,54,52581.16,173.05,Stand-alone empowering benchmark,Leahside,0,Guatemala,2016-01-23 21:15:57,1 699 | 80.15,25,55195.61,214.49,Monitored intermediate circuit,West Wendyland,0,Greenland,2016-07-18 02:51:19,0 700 | 86.76,28,48679.54,189.91,Object-based leadingedge complexity,Lawrenceborough,0,Syrian Arab Republic,2016-02-10 08:21:13,0 701 | 73.88,29,63109.74,233.61,Digitized zero-defect implementation,Kennethview,0,Saint Helena,2016-01-04 06:37:15,0 702 | 58.6,19,44490.09,197.93,Configurable impactful firmware,West Mariafort,1,Lebanon,2016-06-05 21:38:22,0 703 | 69.77,54,57667.99,132.27,Face-to-face dedicated flexibility,Port Sherrystad,0,Malta,2016-06-01 03:17:50,1 704 | 87.27,30,51824.01,204.27,Fully-configurable 5thgeneration circuit,West Melissashire,1,Christmas Island,2016-03-06 06:51:23,1 705 | 77.65,28,66198.66,208.01,Configurable impactful capacity,Pamelamouth,0,Ukraine,2016-02-26 19:35:54,0 706 | 76.02,40,73174.19,219.55,Distributed leadingedge orchestration,Lesliefort,0,Malta,2016-07-13 14:30:14,0 707 | 78.84,26,56593.8,217.66,Persistent even-keeled application,Shawnside,1,Italy,2016-06-29 07:20:46,0 708 | 71.33,23,31072.44,169.4,Optimized attitude-oriented initiative,Josephmouth,0,Japan,2016-03-15 06:54:21,1 709 | 81.9,41,66773.83,225.47,Multi-channeled 3rdgeneration model,Garciatown,0,Mauritius,2016-06-11 06:47:55,0 710 | 46.89,48,72553.94,176.78,Polarized mission-critical structure,Chaseshire,1,Turkey,2016-07-17 13:22:43,1 711 | 77.8,57,43708.88,152.94,Virtual executive implementation,Destinyfurt,0,Namibia,2016-02-14 14:38:01,1 712 | 45.44,43,48453.55,119.27,Enhanced intermediate standardization,Mezaton,0,China,2016-05-04 05:01:37,1 713 | 69.96,31,73413.87,214.06,Realigned tangible collaboration,New Kayla,1,Netherlands,2016-05-20 12:17:28,0 714 | 87.35,35,58114.3,158.29,Cloned dedicated analyzer,Carsonshire,1,Gibraltar,2016-01-26 02:47:17,0 715 | 49.42,53,45465.25,128.0,Ameliorated well-modulated complexity,Jacquelineshire,1,Congo,2016-07-07 18:07:19,1 716 | 71.27,21,50147.72,216.03,Quality-focused bi-directional throughput,South Blakestad,1,Senegal,2016-01-11 12:46:31,0 717 | 49.19,38,61004.51,123.08,Versatile solution-oriented secured line,North Mark,0,Hungary,2016-05-12 12:11:12,1 718 | 39.96,35,53898.89,138.52,Phased leadingedge budgetary management,Kingchester,1,Pitcairn Islands,2016-02-28 23:21:22,1 719 | 85.01,29,59797.64,192.5,Devolved exuding Local Area Network,Evansfurt,0,Slovakia (Slovak Republic),2016-05-03 16:02:50,0 720 | 68.95,51,74623.27,185.85,Front-line bandwidth-monitored capacity,South Adamhaven,1,United States Virgin Islands,2016-03-15 20:19:20,0 721 | 67.59,45,58677.69,113.69,User-centric solution-oriented emulation,Brittanyborough,0,Monaco,2016-07-23 05:21:39,1 722 | 75.71,34,62109.8,246.06,Phased hybrid intranet,Barbershire,0,Portugal,2016-03-11 10:01:23,0 723 | 43.07,36,60583.02,137.63,Monitored zero administration collaboration,East Ericport,1,Turkey,2016-02-11 20:45:46,1 724 | 39.47,43,65576.05,163.48,Team-oriented systematic installation,Crawfordfurt,1,Uganda,2016-07-06 23:09:07,1 725 | 48.22,40,73882.91,214.33,Inverse national core,Turnerville,0,Norfolk Island,2016-03-22 19:14:47,0 726 | 76.76,25,50468.36,230.77,Secured uniform instruction set,Kylieview,1,Niue,2016-05-26 13:28:36,0 727 | 78.74,27,51409.45,234.75,Quality-focused zero tolerance matrices,West Zacharyborough,0,Ukraine,2016-06-18 19:10:14,0 728 | 67.47,24,60514.05,225.05,Multi-tiered heuristic strategy,Watsonfort,1,Vanuatu,2016-03-20 07:12:52,0 729 | 81.17,30,57195.96,231.91,Optimized static archive,Dayton,1,United States Minor Outlying Islands,2016-06-03 07:00:36,0 730 | 89.66,34,52802.58,171.23,Advanced didactic conglomeration,Nicholasport,1,Armenia,2016-02-03 15:15:42,0 731 | 79.6,28,56570.06,227.37,Synergistic discrete middleware,Whitneyfort,1,Sweden,2016-05-03 16:55:02,0 732 | 65.53,19,51049.47,190.17,Pre-emptive client-server installation,Coffeytown,1,Timor-Leste,2016-06-20 02:25:12,0 733 | 61.87,35,66629.61,250.2,Multi-channeled attitude-oriented toolset,North Johnside,1,French Southern Territories,2016-07-10 19:15:52,0 734 | 83.16,41,70185.06,194.95,Decentralized 24hour approach,Robinsonland,0,Finland,2016-01-04 04:00:35,0 735 | 44.11,41,43111.41,121.24,Organic next generation matrix,Lake David,1,Saint Vincent and the Grenadines,2016-04-20 16:49:15,1 736 | 56.57,26,56435.6,131.98,Multi-channeled non-volatile website,West Ericaport,0,Senegal,2016-01-23 13:14:18,1 737 | 83.91,29,53223.58,222.87,Distributed bifurcated challenge,Haleberg,0,Burundi,2016-01-04 22:27:25,0 738 | 79.8,28,57179.91,229.88,Customizable zero-defect Internet solution,West Michaelport,1,Bahamas,2016-04-08 22:40:55,0 739 | 71.23,52,41521.28,122.59,Self-enabling zero administration neural-net,Ericksonmouth,0,Sweden,2016-01-05 11:53:17,1 740 | 47.23,43,73538.09,210.87,Optimized upward-trending productivity,Yangside,1,Svalbard & Jan Mayen Islands,2016-03-17 22:24:02,1 741 | 82.37,30,63664.32,207.44,Open-architected system-worthy ability,Estradafurt,0,Tonga,2016-06-29 04:23:10,0 742 | 43.63,38,61757.12,135.25,Quality-focused maximized extranet,Frankport,1,Korea,2016-05-25 19:45:16,1 743 | 70.9,28,71727.51,190.95,Centralized client-driven workforce,Port Juan,0,Kyrgyz Republic,2016-06-17 23:19:38,0 744 | 71.9,29,72203.96,193.29,De-engineered intangible flexibility,Williamsside,1,Costa Rica,2016-04-24 07:20:16,0 745 | 62.12,37,50671.6,105.86,Re-engineered intangible software,Johnsonview,1,Liechtenstein,2016-03-18 13:00:12,1 746 | 67.35,29,47510.42,118.69,Sharable secondary Graphical User Interface,East Heidi,0,Zimbabwe,2016-04-28 21:58:25,1 747 | 57.99,50,62466.1,124.58,Innovative homogeneous alliance,New Angelview,0,Costa Rica,2016-02-12 08:46:15,1 748 | 66.8,29,59683.16,248.51,Diverse leadingedge website,Lake Brandonview,0,Hungary,2016-07-11 13:23:37,1 749 | 49.13,32,41097.17,120.49,Optimized intermediate help-desk,Morganport,0,Fiji,2016-01-29 00:45:19,1 750 | 45.11,58,39799.73,195.69,Sharable reciprocal project,Browntown,0,Netherlands,2016-01-05 16:26:44,1 751 | 54.35,42,76984.21,164.02,Proactive interactive service-desk,Lake Hailey,0,Sweden,2016-06-20 08:22:09,0 752 | 61.82,59,57877.15,151.93,Open-architected needs-based customer loyalty,Olsonside,1,Barbados,2016-02-06 17:48:28,1 753 | 77.75,31,59047.91,240.64,Multi-lateral motivating circuit,Coxhaven,1,Paraguay,2016-06-22 17:19:09,0 754 | 70.61,28,72154.68,190.12,Assimilated encompassing portal,Meaganfort,0,Italy,2016-04-16 05:24:33,0 755 | 82.72,31,65704.79,179.82,Cross-group global orchestration,North Monicaville,0,Belarus,2016-01-17 05:07:11,0 756 | 76.87,36,72948.76,212.59,Down-sized bandwidth-monitored core,Mullenside,0,South Georgia and the South Sandwich Islands,2016-07-08 22:30:10,0 757 | 65.07,34,73941.91,227.53,Monitored explicit hierarchy,Princebury,1,Anguilla,2016-03-11 00:05:48,0 758 | 56.93,37,57887.64,111.8,Reactive demand-driven strategy,Bradleyside,0,Sierra Leone,2016-06-10 00:35:15,1 759 | 48.86,35,62463.7,128.37,Universal empowering adapter,Elizabethbury,1,Saint Martin,2016-01-04 00:44:57,1 760 | 36.56,29,42838.29,195.89,Team-oriented bi-directional secured line,West Ryan,0,Uganda,2016-01-01 15:14:24,1 761 | 85.73,32,43778.88,147.75,Stand-alone radical throughput,New Tammy,1,Saudi Arabia,2016-07-10 17:24:51,1 762 | 75.81,40,71157.05,229.19,Inverse zero-defect capability,Sanchezland,0,Greenland,2016-03-27 19:50:11,0 763 | 72.94,31,74159.69,190.84,Multi-tiered real-time implementation,Rogerland,0,Venezuela,2016-04-29 13:38:19,0 764 | 53.63,54,50333.72,126.29,Front-line zero-defect array,Vanessaview,1,Liberia,2016-01-08 18:13:43,1 765 | 52.35,25,33293.78,147.61,Mandatory 4thgeneration structure,Jessicashire,1,Mali,2016-06-05 07:54:30,1 766 | 52.84,51,38641.2,121.57,Synergistic asynchronous superstructure,Melissachester,1,Bosnia and Herzegovina,2016-06-29 10:50:45,1 767 | 51.58,33,49822.78,115.91,Vision-oriented system-worthy forecast,Johnsontown,0,Brunei Darussalam,2016-04-24 13:46:10,1 768 | 42.32,29,63891.29,187.09,Digitized radical architecture,New Joshuaport,1,South Georgia and the South Sandwich Islands,2016-02-14 04:14:13,1 769 | 55.04,42,43881.73,106.96,Quality-focused optimizing parallelism,Hernandezside,1,Czech Republic,2016-06-15 05:43:02,1 770 | 68.58,41,13996.5,171.54,Exclusive discrete firmware,New Williamville,1,El Salvador,2016-07-06 12:04:29,1 771 | 85.54,27,48761.14,175.43,Right-sized solution-oriented benchmark,Gilbertville,1,Tokelau,2016-03-31 13:54:51,0 772 | 71.14,30,69758.31,224.82,Assimilated stable encryption,Newmanberg,0,France,2016-06-21 00:52:47,0 773 | 64.38,19,52530.1,180.47,Configurable dynamic secured line,West Alice,1,Gabon,2016-05-27 05:23:26,0 774 | 88.85,40,58363.12,213.96,Cloned optimal leverage,Cannonbury,0,Bulgaria,2016-01-17 18:45:55,0 775 | 66.79,60,60575.99,198.3,Decentralized client-driven data-warehouse,Shelbyport,1,Burkina Faso,2016-04-07 20:34:42,1 776 | 32.6,45,48206.04,185.47,Multi-tiered interactive neural-net,New Henry,0,Mayotte,2016-05-02 18:37:01,1 777 | 43.88,54,31523.09,166.85,Enhanced methodical database,Dustinmouth,1,Somalia,2016-06-04 17:24:07,1 778 | 56.46,26,66187.58,151.63,Ameliorated leadingedge help-desk,South Lisa,0,Albania,2016-04-07 18:52:57,1 779 | 72.18,30,69438.04,225.02,De-engineered attitude-oriented projection,Lisamouth,0,Bolivia,2016-06-10 22:21:10,0 780 | 52.67,44,14775.5,191.26,Persevering 5thgeneration knowledge user,New Hollyberg,0,Jersey,2016-05-19 06:37:38,1 781 | 80.55,35,68016.9,219.91,Extended grid-enabled hierarchy,Port Brittanyville,0,British Virgin Islands,2016-03-28 23:01:24,0 782 | 67.85,41,78520.99,202.7,Reactive tangible contingency,East Ronald,1,Saint Helena,2016-01-21 22:51:34,1 783 | 75.55,36,31998.72,123.71,Decentralized attitude-oriented interface,South Davidmouth,1,Bosnia and Herzegovina,2016-03-12 06:05:12,1 784 | 80.46,29,56909.3,230.78,Mandatory coherent groupware,Carterton,0,India,2016-06-04 09:13:29,0 785 | 82.69,29,61161.29,167.41,Fully-configurable eco-centric frame,Rachelhaven,1,Georgia,2016-05-24 10:16:38,0 786 | 35.21,39,52340.1,154.0,Advanced disintermediate data-warehouse,New Timothy,1,United States Minor Outlying Islands,2016-03-25 06:36:53,1 787 | 36.37,40,47338.94,144.53,Quality-focused zero-defect data-warehouse,North Jessicaville,1,Kiribati,2016-04-22 00:28:18,1 788 | 74.07,22,50950.24,165.43,Cross-group non-volatile secured line,Joneston,1,Ghana,2016-03-22 04:13:35,0 789 | 59.96,33,77143.61,197.66,Expanded modular application,Staceyfort,0,Samoa,2016-01-14 08:27:04,1 790 | 85.62,29,57032.36,195.68,Triple-buffered systematic info-mediaries,South Dianeshire,0,Iran,2016-04-14 21:37:49,0 791 | 40.88,33,48554.45,136.18,Networked non-volatile synergy,West Shannon,1,Costa Rica,2016-05-31 17:50:15,1 792 | 36.98,31,39552.49,167.87,Fully-configurable clear-thinking throughput,Micheletown,1,Northern Mariana Islands,2016-03-17 06:25:47,1 793 | 35.49,47,36884.23,170.04,Front-line actuating functionalities,North Brittanyburgh,0,Liechtenstein,2016-04-13 07:07:36,1 794 | 56.56,26,68783.45,204.47,Compatible composite project,Port Jasmine,1,Grenada,2016-02-03 22:11:13,0 795 | 36.62,32,51119.93,162.44,Customer-focused solution-oriented software,New Sabrina,1,Poland,2016-02-02 19:59:17,1 796 | 49.35,49,44304.13,119.86,Inverse stable synergy,Lake Charlottestad,0,Kenya,2016-04-07 20:38:02,1 797 | 75.64,29,69718.19,204.82,Pre-emptive well-modulated moderator,West Rhondamouth,1,Iran,2016-03-15 19:35:19,0 798 | 79.22,27,63429.18,198.79,Intuitive modular system engine,North Debra,1,Belgium,2016-03-11 12:39:19,0 799 | 77.05,34,65756.36,236.08,Centralized value-added hierarchy,Villanuevastad,0,Namibia,2016-05-17 18:06:46,0 800 | 66.83,46,77871.75,196.17,Assimilated hybrid initiative,North Jeremyport,1,Cyprus,2016-02-28 23:10:32,0 801 | 76.2,24,47258.59,228.81,Optimized coherent Internet solution,Lake Susan,1,Japan,2016-03-02 06:35:08,0 802 | 56.64,29,55984.89,123.24,Versatile 6thgeneration parallelism,Lake John,1,Zimbabwe,2016-02-27 08:52:50,1 803 | 53.33,34,44275.13,111.63,Configurable impactful productivity,Courtneyfort,1,Andorra,2016-03-14 04:34:35,1 804 | 50.63,50,25767.16,142.23,Operative full-range forecast,Tammymouth,0,Luxembourg,2016-03-10 15:07:44,1 805 | 41.84,49,37605.11,139.32,Operative secondary functionalities,Lake Vanessa,0,Cyprus,2016-05-01 08:27:12,1 806 | 53.92,41,25739.09,125.46,Business-focused transitional solution,Lake Amanda,1,Turkey,2016-06-12 11:17:25,1 807 | 83.89,28,60188.38,180.88,Ameliorated intermediate Graphical User Interface,Mariemouth,1,Hong Kong,2016-05-28 12:20:15,0 808 | 55.32,43,67682.32,127.65,Managed 24hour analyzer,Port Douglasborough,0,Netherlands,2016-03-18 09:08:39,1 809 | 53.22,44,44307.18,108.85,Horizontal client-server database,Port Aprilville,0,United States Virgin Islands,2016-05-26 06:03:57,1 810 | 43.16,35,25371.52,156.11,Implemented didactic support,Williamsport,1,Marshall Islands,2016-07-06 03:40:17,1 811 | 67.51,43,23942.61,127.2,Digitized homogeneous core,Lake Faith,0,Western Sahara,2016-04-29 14:10:00,1 812 | 43.16,29,50666.5,143.04,Robust holistic application,Wendyville,1,Saint Vincent and the Grenadines,2016-03-05 20:53:19,1 813 | 79.89,30,50356.06,241.38,Synergized uniform hierarchy,Angelhaven,1,United States of America,2016-05-30 08:35:54,0 814 | 84.25,32,63936.5,170.9,Pre-emptive client-driven secured line,New Sean,1,Angola,2016-04-10 06:32:11,0 815 | 74.18,28,69874.18,203.87,Front-line even-keeled website,Lake Lisa,0,Cayman Islands,2016-01-20 02:31:36,0 816 | 85.78,34,50038.65,232.78,Persistent fault-tolerant service-desk,Valerieland,0,Swaziland,2016-07-20 21:53:42,0 817 | 80.96,39,67866.95,225.0,Integrated leadingedge frame,New Travis,1,Wallis and Futuna,2016-01-17 04:12:30,0 818 | 36.91,48,54645.2,159.69,Ameliorated coherent open architecture,North Samantha,0,Zimbabwe,2016-02-24 07:13:00,1 819 | 54.47,23,46780.09,141.52,Vision-oriented bifurcated contingency,Holderville,0,Chad,2016-03-26 19:37:46,1 820 | 81.98,34,67432.49,212.88,Up-sized maximized model,Patrickmouth,0,Saint Martin,2016-06-04 09:25:27,0 821 | 79.6,39,73392.28,194.23,Organized global flexibility,Lake Deannaborough,0,Rwanda,2016-04-22 07:48:33,0 822 | 57.51,38,47682.28,105.71,Re-engineered zero-defect open architecture,Jeffreymouth,0,Moldova,2016-03-31 08:53:43,1 823 | 82.3,31,56735.83,232.21,Balanced executive definition,Davieshaven,0,Gabon,2016-04-16 08:36:08,0 824 | 73.21,30,51013.37,252.6,Networked logistical info-mediaries,Lake Jessicaville,1,Denmark,2016-05-12 20:57:10,1 825 | 79.09,32,69481.85,209.72,Optimized multimedia website,Hernandezchester,1,Svalbard & Jan Mayen Islands,2016-05-07 21:32:51,0 826 | 68.47,28,67033.34,226.64,Focused coherent success,North Kennethside,0,Poland,2016-06-25 00:33:23,0 827 | 83.69,36,68717.0,192.57,Robust context-sensitive neural-net,Shelbyport,0,Fiji,2016-03-23 05:27:35,0 828 | 83.48,31,59340.99,222.72,Intuitive zero administration adapter,Williamport,1,Philippines,2016-03-04 13:47:47,0 829 | 43.49,45,47968.32,124.67,Synchronized full-range portal,Smithside,0,Vietnam,2016-06-14 12:08:10,1 830 | 66.69,35,48758.92,108.27,Integrated encompassing support,Vanessastad,0,Jersey,2016-05-11 19:13:42,1 831 | 48.46,49,61230.03,132.38,Devolved human-resource circuit,Lisamouth,1,Indonesia,2016-01-21 23:33:22,1 832 | 42.51,30,54755.71,144.77,Grass-roots transitional flexibility,Lake Rhondaburgh,1,Palestinian Territory,2016-01-15 19:45:33,1 833 | 42.83,34,54324.73,132.38,Vision-oriented methodical support,Cunninghamhaven,1,Latvia,2016-04-23 09:42:08,1 834 | 41.46,42,52177.4,128.98,Integrated impactful groupware,Robertstown,1,Malta,2016-05-23 08:06:24,1 835 | 45.99,33,51163.14,124.61,Face-to-face methodical intranet,South Mark,1,Afghanistan,2016-02-27 15:04:52,1 836 | 68.72,27,66861.67,225.97,Fundamental tangible moratorium,New Taylorburgh,0,Austria,2016-02-23 17:37:46,0 837 | 63.11,34,63107.88,254.94,Balanced mobile Local Area Network,Port Karenfurt,1,Micronesia,2016-03-17 22:59:46,0 838 | 49.21,46,49206.4,115.6,Realigned 24/7 core,Carterland,0,Mexico,2016-02-28 03:34:35,1 839 | 55.77,49,55942.04,117.33,Fully-configurable high-level groupware,East Shawn,1,Chile,2016-03-15 14:33:12,1 840 | 44.13,40,33601.84,128.48,Ameliorated discrete extranet,West Derekmouth,1,Cuba,2016-03-03 20:20:32,1 841 | 57.82,46,48867.36,107.56,Centralized asynchronous portal,Brandiland,1,Belarus,2016-04-06 14:16:52,1 842 | 72.46,40,56683.32,113.53,Enhanced tertiary utilization,Cervantesshire,0,Malawi,2016-05-01 09:23:25,1 843 | 61.88,45,38260.89,108.18,Balanced disintermediate conglomeration,North Debrashire,0,Afghanistan,2016-05-30 08:02:27,1 844 | 78.24,23,54106.21,199.29,Sharable value-added solution,Deannaville,0,Luxembourg,2016-04-04 11:39:51,0 845 | 74.61,38,71055.22,231.28,Networked impactful framework,East Christopher,1,South Africa,2016-04-06 23:10:40,0 846 | 89.18,37,46403.18,224.01,Public-key impactful neural-net,Rickymouth,1,Nepal,2016-04-26 21:45:50,0 847 | 44.16,42,61690.93,133.42,Innovative interactive portal,Port Dennis,1,Spain,2016-05-25 00:34:59,1 848 | 55.74,37,26130.93,124.34,Networked asymmetric infrastructure,Lake Michelle,1,Hong Kong,2016-02-11 16:45:41,1 849 | 88.82,36,58638.75,169.1,Assimilated discrete strategy,East Johnport,0,Slovakia (Slovak Republic),2016-01-30 00:05:37,0 850 | 70.39,32,47357.39,261.52,Phased 5thgeneration open system,Sabrinaview,1,Cayman Islands,2016-07-12 10:56:21,0 851 | 59.05,52,50086.17,118.45,Upgradable logistical flexibility,Kristinfurt,1,Uganda,2016-04-23 03:46:34,1 852 | 78.58,33,51772.58,250.11,Centralized user-facing service-desk,Chapmanland,1,Vanuatu,2016-04-16 10:36:49,0 853 | 35.11,35,47638.3,158.03,Extended analyzing emulation,North Jonathan,1,Anguilla,2016-03-11 13:07:30,1 854 | 60.39,45,38987.42,108.25,Front-line methodical utilization,Port Christina,1,Switzerland,2016-03-02 15:39:02,1 855 | 81.56,26,51363.16,213.7,Open-source scalable protocol,Juanport,1,Zimbabwe,2016-07-13 21:31:14,0 856 | 75.03,34,35764.49,255.57,Networked local secured line,East Mike,0,Uruguay,2016-05-29 18:12:00,1 857 | 50.87,24,62939.5,190.41,Programmable empowering orchestration,North Angelatown,0,Liberia,2016-05-10 17:13:47,1 858 | 82.8,30,58776.67,223.2,Enhanced systemic benchmark,West Steven,1,Egypt,2016-05-07 08:39:47,0 859 | 78.51,25,59106.12,205.71,Focused web-enabled Graphical User Interface,Riggsstad,1,Greece,2016-01-17 13:27:13,0 860 | 37.65,51,50457.01,161.29,Automated stable help-desk,Davidview,1,Bahrain,2016-03-09 06:22:03,1 861 | 83.17,43,54251.78,244.4,Managed national hardware,Port Kevinborough,1,Sri Lanka,2016-04-05 18:02:49,0 862 | 91.37,45,51920.49,182.65,Re-engineered composite moratorium,Lawsonshire,1,Kazakhstan,2016-04-01 07:37:18,1 863 | 68.25,29,70324.8,220.08,Phased fault-tolerant definition,Wagnerchester,0,Greenland,2016-02-15 16:18:49,0 864 | 81.32,25,52416.18,165.65,Pre-emptive next generation Internet solution,Daisymouth,0,Moldova,2016-03-08 05:12:57,0 865 | 76.64,39,66217.31,241.5,Reverse-engineered web-enabled support,North Daniel,1,Poland,2016-02-09 23:38:30,0 866 | 74.06,50,60938.73,246.29,Horizontal intermediate monitoring,Port Jacquelinestad,1,Anguilla,2016-06-17 09:38:22,0 867 | 39.53,33,40243.82,142.21,Intuitive transitional artificial intelligence,New Teresa,1,Central African Republic,2016-06-01 12:27:17,1 868 | 86.58,32,60151.77,195.93,Business-focused asynchronous budgetary management,Henryfort,1,Mexico,2016-02-26 23:44:44,0 869 | 90.75,40,45945.88,216.5,Decentralized methodical capability,Lake Joseph,0,Togo,2016-03-11 09:58:32,0 870 | 67.71,25,63430.33,225.76,Synergized intangible open system,Daviesborough,1,Armenia,2016-04-28 02:55:10,0 871 | 82.41,36,65882.81,222.08,Stand-alone logistical service-desk,North Brandon,0,Nicaragua,2016-04-12 04:22:42,0 872 | 45.82,27,64410.8,171.24,Expanded full-range synergy,Adamside,1,Eritrea,2016-02-10 20:43:38,1 873 | 76.79,27,55677.12,235.94,Open-architected intangible strategy,Wademouth,0,Canada,2016-05-01 23:21:53,0 874 | 70.05,33,75560.65,203.44,Diverse directional hardware,North Raymond,0,Croatia,2016-03-24 17:48:31,0 875 | 72.19,32,61067.58,250.32,Balanced discrete approach,Randolphport,1,Switzerland,2016-04-22 19:45:19,0 876 | 77.35,34,72330.57,167.26,Total bi-directional success,East Troyhaven,0,Yemen,2016-03-09 12:10:08,0 877 | 40.34,29,32549.95,173.75,Object-based motivating instruction set,Clarkborough,0,Tokelau,2016-03-30 05:29:38,1 878 | 67.39,44,51257.26,107.19,Realigned intermediate application,Josephberg,0,Armenia,2016-01-24 13:41:38,1 879 | 68.68,34,77220.42,187.03,Sharable encompassing database,Lake Jenniferton,1,Equatorial Guinea,2016-07-15 09:42:19,0 880 | 81.75,43,52520.75,249.45,Progressive 24/7 definition,Lake Jose,0,Barbados,2016-06-07 05:41:16,0 881 | 66.03,22,59422.47,217.37,Pre-emptive next generation strategy,Ashleymouth,0,American Samoa,2016-05-31 23:32:00,0 882 | 47.74,33,22456.04,154.93,Open-source 5thgeneration leverage,Henryland,1,Saint Lucia,2016-05-14 14:49:05,1 883 | 79.18,31,58443.99,236.96,Open-source holistic productivity,Lake Danielle,0,Algeria,2016-01-10 20:18:21,0 884 | 86.81,29,50820.74,199.62,Multi-channeled scalable moratorium,Joshuaburgh,1,Turkmenistan,2016-02-21 16:57:59,0 885 | 41.53,42,67575.12,158.81,Optional tangible productivity,South Jeanneport,0,Mayotte,2016-05-23 00:32:54,1 886 | 70.92,39,66522.79,249.81,Up-sized intangible circuit,New Nathan,1,South Africa,2016-07-21 20:30:06,0 887 | 46.84,45,34903.67,123.22,Virtual homogeneous budgetary management,Jonesshire,0,Macao,2016-05-15 18:44:50,1 888 | 44.4,53,43073.78,140.95,Phased zero-defect portal,Mariahview,1,France,2016-06-30 00:43:40,1 889 | 52.17,44,57594.7,115.37,Optional modular throughput,New Julianberg,1,Equatorial Guinea,2016-02-24 06:17:18,1 890 | 81.45,31,66027.31,205.84,Triple-buffered human-resource complexity,Randyshire,1,Mali,2016-05-30 21:22:22,0 891 | 54.08,36,53012.94,111.02,Innovative cohesive pricing structure,Philipberg,1,Mayotte,2016-06-02 04:14:37,1 892 | 76.65,31,61117.5,238.43,Function-based executive moderator,West Dennis,0,Pakistan,2016-04-18 07:00:38,0 893 | 54.39,20,52563.22,171.9,Digitized content-based circuit,Richardshire,1,Guadeloupe,2016-02-29 18:06:21,1 894 | 37.74,40,65773.49,190.95,Balanced uniform algorithm,Lake James,0,Denmark,2016-05-27 12:45:37,1 895 | 69.86,25,50506.44,241.36,Triple-buffered foreground encryption,Austinborough,0,New Zealand,2016-01-12 21:17:15,0 896 | 85.37,36,66262.59,194.56,Front-line system-worthy flexibility,Alexandrafort,1,Netherlands Antilles,2016-01-27 17:08:19,0 897 | 80.99,26,35521.88,207.53,Centralized clear-thinking Graphic Interface,Melissastad,1,Belarus,2016-06-10 03:56:41,0 898 | 78.84,32,62430.55,235.29,Optimized 5thgeneration moratorium,Gonzalezburgh,1,Taiwan,2016-04-09 09:26:39,0 899 | 77.36,41,49597.08,115.79,Fully-configurable asynchronous firmware,Port Jennifer,0,El Salvador,2016-02-26 06:00:16,1 900 | 55.46,37,42078.89,108.1,Exclusive systematic algorithm,Chrismouth,0,Taiwan,2016-02-21 23:07:11,1 901 | 35.66,45,46197.59,151.72,Exclusive cohesive intranet,Port Beth,0,Peru,2016-04-29 14:08:26,1 902 | 50.78,51,49957.0,122.04,Vision-oriented asynchronous Internet solution,West David,0,Liberia,2016-02-11 17:02:07,1 903 | 40.47,38,24078.93,203.9,Sharable 5thgeneration access,Fraziershire,0,Burundi,2016-07-22 07:44:43,1 904 | 45.62,43,53647.81,121.28,Monitored homogeneous artificial intelligence,Robertfurt,0,Macao,2016-06-26 02:34:15,1 905 | 84.76,30,61039.13,178.69,Monitored 24/7 moratorium,South Pamela,0,Venezuela,2016-05-14 23:08:14,0 906 | 80.64,26,46974.15,221.59,Vision-oriented real-time framework,North Laurenview,0,Luxembourg,2016-05-24 10:04:39,0 907 | 75.94,27,53042.51,236.96,Future-proofed stable function,Campbellstad,1,Italy,2016-02-16 12:05:45,0 908 | 37.01,50,48826.14,216.01,Secured encompassing Graphical User Interface,Port Derekberg,0,San Marino,2016-03-20 02:44:13,1 909 | 87.18,31,58287.86,193.6,Right-sized logistical middleware,West Andrew,0,Madagascar,2016-01-31 05:12:44,0 910 | 56.91,50,21773.22,146.44,Team-oriented executive core,West Randy,0,Norfolk Island,2016-04-01 05:17:28,1 911 | 75.24,24,52252.91,226.49,Vision-oriented next generation solution,South Christopher,0,Vanuatu,2016-02-25 16:33:24,0 912 | 42.84,52,27073.27,182.2,Enhanced optimizing website,Lake Michellebury,1,Tunisia,2016-03-21 11:02:49,1 913 | 67.56,47,50628.31,109.98,Reduced background data-warehouse,Zacharyton,0,Paraguay,2016-02-12 05:20:19,1 914 | 34.96,42,36913.51,160.49,Right-sized mobile initiative,West James,1,Macedonia,2016-06-01 16:10:30,1 915 | 87.46,37,61009.1,211.56,Synergized grid-enabled framework,Millerview,1,Heard Island and McDonald Islands,2016-06-16 03:17:45,0 916 | 41.86,39,53041.77,128.62,Open-source stable paradigm,Hawkinsbury,1,Ethiopia,2016-03-26 15:28:07,1 917 | 34.04,34,40182.84,174.88,Reverse-engineered context-sensitive emulation,Elizabethport,1,El Salvador,2016-02-16 07:37:28,1 918 | 54.96,42,59419.78,113.75,Public-key disintermediate emulation,West Amanda,1,Niger,2016-02-28 09:31:31,1 919 | 87.14,31,58235.21,199.4,Up-sized bifurcated capability,Wadestad,1,Timor-Leste,2016-05-18 01:00:52,0 920 | 78.79,32,68324.48,215.29,Stand-alone background open system,Mauriceshire,1,Uruguay,2016-02-21 13:11:08,0 921 | 65.56,25,69646.35,181.25,Stand-alone explicit orchestration,West Arielstad,1,Somalia,2016-01-05 12:59:07,0 922 | 81.05,34,54045.39,245.5,Configurable asynchronous application,Adamsstad,0,Malaysia,2016-05-18 00:07:43,0 923 | 55.71,37,57806.03,112.52,Upgradable 4thgeneration portal,Lake James,1,Korea,2016-03-06 23:26:44,1 924 | 45.48,49,53336.76,129.16,Networked client-server solution,Blairborough,1,Lao People's Democratic Republic,2016-05-19 04:23:41,1 925 | 47.0,56,50491.45,149.53,Public-key bi-directional Graphical User Interface,New Marcusbury,0,Bahamas,2016-04-29 20:40:21,1 926 | 59.64,51,71455.62,153.12,Re-contextualized human-resource success,Evansville,1,Guyana,2016-05-03 01:09:01,1 927 | 35.98,45,43241.88,150.79,Front-line fresh-thinking installation,Huffmanchester,0,Ethiopia,2016-06-27 21:51:47,1 928 | 72.55,22,58953.01,202.34,Balanced empowering success,New Cynthia,0,Bosnia and Herzegovina,2016-02-08 07:33:22,0 929 | 91.15,38,36834.04,184.98,Robust uniform framework,Joshuamouth,0,Cyprus,2016-02-22 07:04:05,0 930 | 80.53,29,66345.1,187.64,Sharable upward-trending support,West Benjamin,0,Singapore,2016-03-21 08:13:24,0 931 | 82.49,45,38645.4,130.84,Assimilated multi-state paradigm,Williamsfort,0,Dominican Republic,2016-05-31 00:58:37,1 932 | 80.94,36,60803.0,239.94,Self-enabling local strategy,North Tiffany,0,Bermuda,2016-01-01 05:31:22,0 933 | 61.76,34,33553.9,114.69,Open-source local approach,Edwardsport,0,Jamaica,2016-05-27 08:53:51,1 934 | 63.3,38,63071.34,116.19,Polarized intangible encoding,Lake Evantown,0,Saint Barthelemy,2016-05-09 07:13:27,1 935 | 36.73,34,46737.34,149.79,Multi-lateral attitude-oriented adapter,South Henry,1,Albania,2016-06-27 01:56:36,1 936 | 78.41,33,55368.67,248.23,Multi-lateral 24/7 Internet solution,Harmonhaven,1,Mozambique,2016-06-03 04:51:46,0 937 | 83.98,36,68305.91,194.62,Profit-focused secondary portal,West Gregburgh,0,Zimbabwe,2016-02-24 00:44:44,0 938 | 63.18,45,39211.49,107.92,Reactive upward-trending migration,Hansenland,0,Georgia,2016-03-05 12:03:41,1 939 | 50.6,48,65956.71,135.67,Customer-focused fault-tolerant implementation,Port Michaelmouth,0,Brazil,2016-01-15 22:49:45,1 940 | 32.6,38,40159.2,190.05,Customizable homogeneous contingency,Tylerport,0,Syrian Arab Republic,2016-02-12 03:39:09,1 941 | 60.83,19,40478.83,185.46,Versatile next generation pricing structure,West Lacey,1,Palestinian Territory,2016-02-19 20:49:27,0 942 | 44.72,46,40468.53,123.86,Cross-group systemic customer loyalty,North Jenniferburgh,1,Grenada,2016-03-12 02:48:18,1 943 | 78.76,51,66980.27,162.05,Face-to-face modular budgetary management,South Davidhaven,0,Ghana,2016-07-23 04:04:42,1 944 | 79.51,39,34942.26,125.11,Proactive non-volatile encryption,North Charlesbury,1,Brunei Darussalam,2016-03-06 09:33:46,1 945 | 39.3,32,48335.2,145.73,Decentralized bottom-line help-desk,Jonathanland,0,Lithuania,2016-02-24 04:11:37,1 946 | 64.79,30,42251.59,116.07,Visionary mission-critical application,North Virginia,0,Maldives,2016-02-17 20:22:49,1 947 | 89.8,36,57330.43,198.24,User-centric attitude-oriented adapter,West Tanner,0,Lesotho,2016-02-02 04:57:50,0 948 | 72.82,34,75769.82,191.82,User-centric discrete success,Jonesmouth,1,Czech Republic,2016-01-27 16:06:05,0 949 | 38.65,31,51812.71,154.77,Total even-keeled architecture,Port Jason,1,Iceland,2016-05-24 09:50:41,1 950 | 59.01,30,75265.96,178.75,Focused multimedia implementation,West Annefort,1,Philippines,2016-02-08 22:45:26,1 951 | 78.96,50,69868.48,193.15,Stand-alone well-modulated product,East Jason,0,Cayman Islands,2016-02-12 01:55:38,1 952 | 63.99,43,72802.42,138.46,Ameliorated bandwidth-monitored contingency,North Cassie,0,Haiti,2016-01-11 08:18:12,1 953 | 41.35,27,39193.45,162.46,Streamlined homogeneous analyzer,Hintonport,1,Colombia,2016-03-03 03:51:27,1 954 | 62.79,36,18368.57,231.87,Total coherent archive,New James,1,Luxembourg,2016-05-30 20:08:51,1 955 | 45.53,29,56129.89,141.58,Front-line neutral alliance,North Destiny,0,United Arab Emirates,2016-04-22 22:01:21,1 956 | 51.65,31,58996.56,249.99,Virtual context-sensitive support,Mclaughlinbury,0,Ireland,2016-05-25 10:39:28,0 957 | 54.55,44,41547.62,109.04,Re-engineered optimal policy,West Gabriellamouth,0,Canada,2016-02-04 03:10:17,1 958 | 35.66,36,59240.24,172.57,Implemented uniform synergy,Alvarezland,0,Svalbard & Jan Mayen Islands,2016-02-21 20:09:12,1 959 | 69.95,28,56725.47,247.01,Horizontal even-keeled challenge,New Julie,0,Malta,2016-04-28 01:24:34,0 960 | 79.83,29,55764.43,234.23,Innovative regional groupware,North Frankstad,1,Sudan,2016-05-18 19:33:51,0 961 | 85.35,37,64235.51,161.42,Exclusive multi-state Internet solution,Claytonside,1,Ecuador,2016-02-17 11:15:31,0 962 | 56.78,28,39939.39,124.32,Mandatory empowering focus group,Melanieton,0,Senegal,2016-06-19 23:04:45,1 963 | 78.67,26,63319.99,195.56,Proactive 5thgeneration frame,Lake Michaelport,0,Cambodia,2016-02-20 09:54:06,0 964 | 70.09,21,54725.87,211.17,Automated full-range Internet solution,East Benjaminville,0,Belarus,2016-01-22 12:58:14,0 965 | 60.75,42,69775.75,247.05,Fully-configurable systemic productivity,Garrettborough,1,Guyana,2016-02-19 13:26:24,0 966 | 65.07,24,57545.56,233.85,Multi-lateral multi-state encryption,Port Raymondfort,0,Mali,2016-01-03 07:13:53,0 967 | 35.25,50,47051.02,194.44,Intuitive global website,Waltertown,0,Iran,2016-01-03 04:39:47,1 968 | 37.58,52,51600.47,176.7,Exclusive disintermediate Internet solution,Cameronberg,1,Bulgaria,2016-04-13 13:04:47,1 969 | 68.01,25,68357.96,188.32,Ameliorated actuating workforce,Kaylashire,1,Afghanistan,2016-01-01 03:35:35,0 970 | 45.08,38,35349.26,125.27,Synergized clear-thinking protocol,Fosterside,0,Liberia,2016-03-27 08:32:37,1 971 | 63.04,27,69784.85,159.05,Triple-buffered multi-state complexity,Davidstad,0,Netherlands Antilles,2016-07-10 16:25:56,1 972 | 40.18,29,50760.23,151.96,Enhanced intangible portal,Lake Tracy,0,Hong Kong,2016-06-25 04:21:33,1 973 | 45.17,48,34418.09,132.07,Down-sized background groupware,Taylormouth,1,Palau,2016-01-27 14:41:10,1 974 | 50.48,50,20592.99,162.43,Switchable real-time product,Dianaville,0,Malawi,2016-05-16 18:51:59,1 975 | 80.87,28,63528.8,203.3,Ameliorated local workforce,Collinsburgh,0,Uruguay,2016-02-27 20:20:25,0 976 | 41.88,40,44217.68,126.11,Streamlined exuding adapter,Port Rachel,1,Cyprus,2016-02-28 23:54:44,1 977 | 39.87,48,47929.83,139.34,Business-focused user-facing benchmark,South Rebecca,1,Mexico,2016-06-13 06:11:33,1 978 | 61.84,45,46024.29,105.63,Reactive bi-directional standardization,Port Joshuafort,1,Niger,2016-05-05 11:07:13,1 979 | 54.97,31,51900.03,116.38,Virtual bifurcated portal,Robinsontown,1,France,2016-07-07 12:17:33,1 980 | 71.4,30,72188.9,166.31,Integrated 3rdgeneration monitoring,Beckton,0,Japan,2016-05-24 17:07:08,0 981 | 70.29,31,56974.51,254.65,Balanced responsive open system,New Frankshire,1,Norfolk Island,2016-03-30 14:36:55,0 982 | 67.26,57,25682.65,168.41,Focused incremental Graphic Interface,North Derekville,1,Bulgaria,2016-05-27 05:54:03,1 983 | 76.58,46,41884.64,258.26,Secured 24hour policy,West Sydney,0,Uzbekistan,2016-01-03 16:30:51,0 984 | 54.37,38,72196.29,140.77,Up-sized asymmetric firmware,Lake Matthew,0,Mexico,2016-06-25 18:17:53,1 985 | 82.79,32,54429.17,234.81,Distributed fault-tolerant service-desk,Lake Zacharyfurt,1,Brunei Darussalam,2016-02-24 10:36:43,0 986 | 66.47,31,58037.66,256.39,Vision-oriented human-resource synergy,Lindsaymouth,1,France,2016-03-03 03:13:48,0 987 | 72.88,44,64011.26,125.12,Customer-focused explicit challenge,Sarahland,0,Yemen,2016-04-21 19:56:24,1 988 | 76.44,28,59967.19,232.68,Synchronized human-resource moderator,Port Julie,1,Northern Mariana Islands,2016-04-06 17:26:37,0 989 | 63.37,43,43155.19,105.04,Open-architected full-range projection,Michaelshire,1,Poland,2016-03-23 12:53:23,1 990 | 89.71,48,51501.38,204.4,Versatile local forecast,Sarafurt,1,Bahrain,2016-02-17 07:00:38,0 991 | 70.96,31,55187.85,256.4,Ameliorated user-facing help-desk,South Denise,0,Saint Pierre and Miquelon,2016-06-26 07:01:47,0 992 | 35.79,44,33813.08,165.62,Enterprise-wide tangible model,North Katie,1,Tonga,2016-04-20 13:36:42,1 993 | 38.96,38,36497.22,140.67,Versatile mission-critical application,Mauricefurt,1,Comoros,2016-07-21 16:02:40,1 994 | 69.17,40,66193.81,123.62,Extended leadingedge solution,New Patrick,0,Montenegro,2016-03-06 11:36:06,1 995 | 64.2,27,66200.96,227.63,Phased zero tolerance extranet,Edwardsmouth,1,Isle of Man,2016-02-11 23:45:01,0 996 | 43.7,28,63126.96,173.01,Front-line bifurcated ability,Nicholasland,0,Mayotte,2016-04-04 03:57:48,1 997 | 72.97,30,71384.57,208.58,Fundamental modular algorithm,Duffystad,1,Lebanon,2016-02-11 21:49:00,1 998 | 51.3,45,67782.17,134.42,Grass-roots cohesive monitoring,New Darlene,1,Bosnia and Herzegovina,2016-04-22 02:07:01,1 999 | 51.63,51,42415.72,120.37,Expanded intangible solution,South Jessica,1,Mongolia,2016-02-01 17:24:57,1 1000 | 55.55,19,41920.79,187.95,Proactive bandwidth-monitored policy,West Steven,0,Guatemala,2016-03-24 02:35:54,0 1001 | 45.01,26,29875.8,178.35,Virtual 5thgeneration emulation,Ronniemouth,0,Brazil,2016-06-03 21:43:21,1 1002 | --------------------------------------------------------------------------------