├── .gitignore ├── eda ├── corr.png ├── sankey1.JPG ├── sankey2.JPG └── scatter.png ├── d3 ├── data │ ├── fonts │ │ ├── OpenSans-Bold.ttf │ │ ├── OpenSans-Italic.ttf │ │ ├── OpenSans-Light.ttf │ │ ├── OpenSans-Regular.ttf │ │ ├── OpenSans-ExtraBold.ttf │ │ ├── OpenSans-SemiBold.ttf │ │ ├── OpenSans-BoldItalic.ttf │ │ ├── OpenSans-LightItalic.ttf │ │ ├── OpenSans-SemiBoldItalic.ttf │ │ └── OpenSans-ExtraBoldItalic.ttf │ ├── states.csv │ ├── funding-data.csv │ └── us-states.json ├── index.html └── main.js ├── sketches ├── Final Sketches │ ├── Slide1.JPG │ ├── Slide2.JPG │ ├── Slide3.JPG │ ├── Slide4.JPG │ └── Slide5.JPG └── Initial Sketches │ ├── sketch_1.jpg │ ├── sketch_2.jpg │ └── sketch_3.jpg ├── src ├── __pycache__ │ └── flask_roc_v2.cpython-36.pyc └── flask_roc_v2.py └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | ./src/__pycache__ 2 | -------------------------------------------------------------------------------- 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"Iowa","IA" 18 | "Kansas","KS" 19 | "Kentucky","KY" 20 | "Louisiana","LA" 21 | "Maine","ME" 22 | "Montana","MT" 23 | "Nebraska","NE" 24 | "Nevada","NV" 25 | "New Hampshire","NH" 26 | "New Jersey","NJ" 27 | "New Mexico","NM" 28 | "New York","NY" 29 | "North Carolina","NC" 30 | "North Dakota","ND" 31 | "Ohio","OH" 32 | "Oklahoma","OK" 33 | "Oregon","OR" 34 | "Maryland","MD" 35 | "Massachusetts","MA" 36 | "Michigan","MI" 37 | "Minnesota","MN" 38 | "Mississippi","MS" 39 | "Missouri","MO" 40 | "Pennsylvania","PA" 41 | "Rhode Island","RI" 42 | "South Carolina","SC" 43 | "South Dakota","SD" 44 | "Tennessee","TN" 45 | "Texas","TX" 46 | "Utah","UT" 47 | "Vermont","VT" 48 | "Virginia","VA" 49 | "Washington","WA" 50 | "West Virginia","WV" 51 | "Wisconsin","WI" 52 | "Wyoming","WY" 53 | -------------------------------------------------------------------------------- /d3/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 16 | 45 | 89 | 90 | Client-Server Visualization and Modeling 91 | 92 | 93 | 94 | 95 | 101 |

Startup Success Prediction: Model development and Analysis

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195 | 196 | 197 | 198 | 199 | 200 | -------------------------------------------------------------------------------- /src/flask_roc_v2.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | from sklearn.metrics import roc_curve 4 | from sklearn import metrics 5 | from flask_cors import CORS 6 | import simplejson as json 7 | from flask import Flask, stream_with_context 8 | from sklearn.cross_validation import train_test_split 9 | from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier 10 | from io import StringIO 11 | from werkzeug.datastructures import Headers 12 | from werkzeug.wrappers import Response 13 | import csv 14 | from sklearn.tree import DecisionTreeClassifier 15 | 16 | app = Flask(__name__) 17 | CORS(app) 18 | 19 | @app.route('/roc_curve/m=/d=/l=/n=/c=', methods=['GET']) 20 | def roc_curve(m,d,l,n,c): 21 | if m == "model1": 22 | model = GradientBoostingClassifier(criterion = 'mae', learning_rate = float(c), 23 | loss = 'deviance', max_depth = int(d), 24 | max_features = 'log2', max_leaf_nodes = int(l), 25 | n_estimators = int(n)) 26 | elif m == "model2": 27 | model = RandomForestClassifier(criterion = 'entropy', max_depth = int(d), 28 | max_features = 'sqrt', max_leaf_nodes = int(l), 29 | n_estimators = int(n)) 30 | else: 31 | model = DecisionTreeClassifier(max_depth = int(d), 32 | max_leaf_nodes = int(l)) 33 | 34 | print("Roc",m,d,l,n,c) 35 | classifier = model.fit(X_train, y_train) 36 | print(model) 37 | y_pred = classifier.predict_proba(X_test) 38 | fpr, tpr, thresholds = metrics.roc_curve(y_test,y_pred[:,1],pos_label=1) 39 | result=[] 40 | for i in range(len(fpr)): 41 | result.append({"fpr":fpr[i],"tpr":tpr[i]}) 42 | return json.dumps(result) 43 | 44 | @app.route('/viz/m=/d=/l=/n=/c=', methods=['GET']) 45 | def viz(m,d,l,n,c): 46 | if m == "model1": 47 | model = GradientBoostingClassifier(criterion = 'mae', learning_rate = float(c), 48 | loss = 'deviance', max_depth = int(d), 49 | max_features = 'log2', max_leaf_nodes = int(l), 50 | n_estimators = int(n)) 51 | elif m == "model2": 52 | model = RandomForestClassifier(criterion = 'entropy', max_depth = int(d), 53 | max_features = None, max_leaf_nodes = int(l), 54 | n_estimators = int(n)) 55 | else: 56 | model = DecisionTreeClassifier(max_depth = int(d), 57 | max_leaf_nodes = int(l)) 58 | 59 | print("Viz",m,d,l,n,c) 60 | 61 | classifier = model.fit(X_train, y_train) 62 | print(model) 63 | X_new = test_df[features].values 64 | 65 | test_df['prob_acquired'] = classifier.predict_proba(X_new)[:,1] 66 | test_df['acquired'] = np.where(test_df['prob_acquired']>=0.5, 1, 0) 67 | test_df['closed'] = np.where(test_df['prob_acquired']<0.5, 1, 0) 68 | 69 | q1 = pd.merge(states_df, test_df, left_on = 'State', right_on = 'state', how = 'left') 70 | 71 | ren_col = {'prob_acquired': 'mean_prob_companies_acquired_by_state', 72 | 'acquired': 'count_prob_companies_acquired_by_state', 73 | 'closed': 'count_prob_companies_closed_by_state'} 74 | output_df = q1.groupby('State').agg({'prob_acquired': 'mean', 75 | 'acquired': 'sum', 76 | 'closed': 'sum'}).rename(columns = ren_col) 77 | output_df = output_df.reset_index() 78 | 79 | output_df.loc[output_df['mean_prob_companies_acquired_by_state'].isnull(), 'mean_prob_companies_acquired_by_state'] = 0 80 | output_df.loc[output_df['count_prob_companies_acquired_by_state'].isnull(), 'count_prob_companies_acquired_by_state'] = 0 81 | output_df.loc[output_df['count_prob_companies_closed_by_state'].isnull(), 'count_prob_companies_closed_by_state'] = 0 82 | 83 | def generate(output_df): 84 | d = StringIO() 85 | w = csv.writer(d) 86 | 87 | #write header 88 | w.writerow(tuple(output_df.columns)) 89 | yield d.getvalue() 90 | d.seek(0) 91 | d.truncate(0) 92 | 93 | for i in range(output_df.shape[0]): 94 | w.writerow(tuple(output_df.iloc[i].values)) 95 | yield d.getvalue() 96 | d.seek(0) 97 | d.truncate(0) 98 | 99 | # add a filename 100 | headers = Headers() 101 | headers.set('Content-Disposition', 'attachment', filename='log.csv') 102 | 103 | # stream the response as the data is generated 104 | return Response( 105 | stream_with_context(generate(output_df)), 106 | mimetype='text/csv', headers=headers 107 | ) 108 | 109 | 110 | train_df = pd.read_csv('../d3/data/train-data.csv', encoding = 'utf-8') 111 | test_df = pd.read_csv('../d3/data/test-data.csv', encoding = 'utf-8') 112 | states_df = pd.read_csv('../d3/data/states.csv', encoding = 'utf-8') 113 | 114 | features = ['age_first_funding_year', 'age_last_funding_year', 'relationships', 115 | 'funding_total_usd', 'milestones', 'is_CA', 'is_NY', 'is_MA', 'is_TX', 116 | 'is_otherstate', 'is_software', 'is_web', 'is_mobile', 'is_enterprise', 117 | 'is_advertising', 'is_gamesvideo', 'is_ecommerce', 'is_biotech', 118 | 'is_consulting', 'is_othercategory', 'has_VC', 'has_angel', 'has_roundA', 119 | 'has_roundB', 'has_roundC', 'has_roundD', 'avg_participants', 120 | 'is_top500','funding_rounds'] 121 | data = train_df[features + ['labels']].values 122 | 123 | X = data[:,:-1] 124 | y = data[:,29] 125 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0) 126 | 127 | app.run(debug = True) -------------------------------------------------------------------------------- /d3/main.js: -------------------------------------------------------------------------------- 1 | 2 | function chooseModel(){ 3 | var x = document.getElementById("modelname").value; 4 | 5 | if (x === "model2"){ 6 | document.getElementById("smallbox").style.visibility="hidden"; 7 | document.getElementById("smallbox4").style.visibility="visible"; 8 | } 9 | if (x === "model3") { 10 | document.getElementById("smallbox").style.visibility="hidden"; 11 | document.getElementById("smallbox4").style.visibility="hidden"; 12 | } 13 | if (x === "model1") { 14 | document.getElementById("smallbox").style.visibility="visible"; 15 | document.getElementById("smallbox4").style.visibility="visible"; 16 | } 17 | } 18 | 19 | function connectFlask() { 20 | var url = "http://127.0.0.1:5000/roc_curve/" 21 | var modelname = document.getElementById("modelname").value; 22 | var max_depth = document.getElementById("depth").value; 23 | var max_leaf_nodes = document.getElementById("leaf").value; 24 | var n_estimators = document.getElementById("estimators").value; 25 | var learning_rate = document.getElementById("lr").value; 26 | 27 | if (learning_rate==="") { 28 | learning_rate = .1; 29 | } 30 | 31 | if (n_estimators==="") { 32 | n_estimators = 100; 33 | } 34 | 35 | if (max_depth==="") { 36 | max_depth = 5; 37 | } 38 | 39 | if (max_leaf_nodes==="") { 40 | max_leaf_nodes = 10; 41 | } 42 | 43 | url1 = url.concat("m=").concat(modelname).concat("/d=").concat(max_depth).concat("/l=").concat(max_leaf_nodes).concat("/n=").concat(n_estimators).concat("/c=").concat(learning_rate); 44 | 45 | 46 | d3.json(url1).then(function(data) { 47 | drawRoc(data); 48 | }); 49 | } 50 | 51 | 52 | 53 | 54 | function drawRoc(data){ 55 | 56 | var w = 800; 57 | var h = 450; 58 | var padding = 50; 59 | 60 | var xScale = d3.scaleLinear() 61 | .domain([0,1]) 62 | .range([0, w/2]) 63 | 64 | var yScale = d3.scaleLinear() 65 | .domain([0,1]) 66 | .range([h-h/10, 0]); 67 | 68 | //d3.select("#chart1") 69 | // .selectAll("*") 70 | // .remove(); 71 | 72 | var svg = d3.select("#chart1") 73 | .append("svg") 74 | .attr("width", w + padding) 75 | .attr("height", h + padding) 76 | .attr("align","center-right") 77 | .append("g") 78 | .attr("transform", "translate(" + 275 + "," + padding + ")"); 79 | 80 | svg.append("g") 81 | .attr("class", "x axis") 82 | .attr("transform", "translate(0," + (h-h/10) + ")") 83 | .call(d3.axisBottom(xScale)); 84 | 85 | svg.append("g") 86 | .attr("class", "y axis") 87 | .call(d3.axisLeft(yScale)); 88 | 89 | var rocline = d3.line() 90 | .x(function(d) { return xScale(d.fpr)}) 91 | .y(function(d) { return yScale(d.tpr)}); 92 | 93 | svg.append("path") 94 | .datum(data) 95 | .attr("class", "line") 96 | .attr("d", rocline); 97 | 98 | svg.append("line") 99 | .attr("x1", xScale(0)) 100 | .attr("y1", yScale(0)) 101 | .attr("x2", xScale(1)) 102 | .attr("y2", yScale(1)) 103 | .attr("stroke-width", 2) 104 | .attr("stroke", "black") 105 | .attr("stroke-dasharray", "8,8"); 106 | 107 | 108 | svg.append("text") 109 | .attr("transform", "rotate(0)") 110 | .attr("y", (padding + 380)) 111 | .attr("x",180) 112 | .attr("dy", "1em") 113 | .text("False Positive Rate "); 114 | 115 | svg.append("text") 116 | .attr("transform", "rotate(-90)") 117 | .attr("y", 0 - (padding/.9)) 118 | .attr("x",0 - (h / 2)) 119 | .attr("dy", "1em") 120 | .text("True Positive Rate "); 121 | } 122 | 123 | 124 | function visualize(){ 125 | 126 | //Width and height 127 | var w = 1200; 128 | var h = 600; 129 | //Define map projection 130 | var projection = d3.geoAlbersUsa() 131 | .translate([w/2-w/12, h/2]) 132 | .scale([1200]); 133 | //Define path generator 134 | var path = d3.geoPath() 135 | .projection(projection); 136 | 137 | //Define quantize scale to sort data values into buckets of color 138 | var color = d3.scaleQuantize() 139 | .range(['rgb(215,25,28)','rgb(253,174,97)','rgb(255,255,191)','rgb(166,217,106)','rgb(26,150,65)']); 140 | //Colors derived from ColorBrewer, by Cynthia Brewer, and included in 141 | //https://github.com/d3/d3-scale-chromatic 142 | 143 | //Remove previous svg elements 144 | d3.select("#chart1") 145 | .selectAll("*") 146 | .remove(); 147 | //Create SVG element 148 | var svg = d3.select("#chart1") 149 | .append("svg") 150 | .attr("width", w) 151 | .attr("height", h); 152 | 153 | var url = "http://127.0.0.1:5000/viz/" 154 | var modelname = document.getElementById("modelname").value; 155 | var learning_rate = document.getElementById("lr").value; 156 | var max_depth = document.getElementById("depth").value; 157 | var max_leaf_nodes = document.getElementById("leaf").value; 158 | var n_estimators = document.getElementById("estimators").value; 159 | 160 | if (learning_rate==="") { 161 | learning_rate = .1; 162 | } 163 | 164 | if (n_estimators==="") { 165 | n_estimators = 100; 166 | } 167 | 168 | if (max_depth==="") { 169 | max_depth = 5; 170 | } 171 | 172 | if (max_leaf_nodes==="") { 173 | max_leaf_nodes = 10; 174 | } 175 | 176 | url1 = url.concat("m=").concat(modelname).concat("/d=").concat(max_depth).concat("/l=").concat(max_leaf_nodes).concat("/n=").concat(n_estimators).concat("/c=").concat(learning_rate); 177 | 178 | 179 | d3.csv(url1).then(function(data) { 180 | //Set input domain for color scale 181 | color.domain([ 182 | d3.min(data, function(d) { return d.mean_prob_companies_acquired_by_state; }), 183 | d3.max(data, function(d) { return d.mean_prob_companies_acquired_by_state; }) 184 | ]); 185 | //Load in GeoJSON data 186 | d3.json("./data/us-states.json").then(function(json) { 187 | //Merge the ag. data and GeoJSON 188 | //Loop through once for each ag. data value 189 | for (var i = 0; i < data.length; i++) { 190 | 191 | //Grab state name 192 | var dataState = data[i].State; 193 | 194 | //Grab data value, and convert from string to float 195 | var dataValue = parseFloat(data[i].mean_prob_companies_acquired_by_state); 196 | 197 | //Grab data value, and convert from string to float 198 | var dataAcquired = parseFloat(data[i].count_prob_companies_acquired_by_state); 199 | 200 | //Grab data value, and convert from string to float 201 | var dataClosed = parseFloat(data[i].count_prob_companies_closed_by_state); 202 | 203 | 204 | //Find the corresponding state inside the GeoJSON 205 | for (var j = 0; j < json.features.length; j++) { 206 | 207 | var jsonState = json.features[j].properties.name; 208 | 209 | if (dataState == jsonState) { 210 | 211 | //Copy the data value into the JSON 212 | json.features[j].properties.value = dataValue; 213 | json.features[j].properties.acquired = dataAcquired; 214 | json.features[j].properties.closed = dataClosed; 215 | 216 | //Stop looking through the JSON 217 | break; 218 | 219 | } 220 | } 221 | } 222 | //Bind data and create one path per GeoJSON feature 223 | svg.selectAll("path") 224 | .data(json.features) 225 | .enter() 226 | .append("path") 227 | .attr("d", path) 228 | .style("fill", function(d) { 229 | //Get data value 230 | var value = d.properties.value; 231 | 232 | if (value) { 233 | //If value exists… 234 | return color(value); 235 | } else { 236 | //If value is undefined… 237 | return "#ccc"; 238 | } 239 | }) 240 | .style("fill-opacity", 1) 241 | .style("stroke", "#a9a9a9") 242 | .style("stroke-width", 1.5) 243 | .on("mouseover", function(d) { 244 | //Get this bar's x/y values, then augment for the tooltip 245 | //Update the tooltip position and value 246 | var coordinates = d3.mouse(this); 247 | var xPosition = coordinates[0] + w/15; 248 | var yPosition = coordinates[1] + h/3; 249 | var format = d3.format(".4f"); 250 | 251 | d3.select("#tooltip") 252 | .style("left", xPosition + "px") 253 | .style("top", yPosition + "px") 254 | .select("#state") 255 | .text(d.properties.name); 256 | d3.select("#tooltip") 257 | .style("left", xPosition + "px") 258 | .style("top", yPosition + "px") 259 | .select("#acquired") 260 | .text("Startups predicted to be successful: " + d.properties.acquired); 261 | d3.select("#tooltip") 262 | .style("left", xPosition + "px") 263 | .style("top", yPosition + "px") 264 | .select("#closed") 265 | .text("Startups predicted to fail: " + d.properties.closed); 266 | d3.select("#tooltip") 267 | .style("left", xPosition + "px") 268 | .style("top", yPosition + "px") 269 | .select("#prob") 270 | .text("Mean probability of startups to be successful: " + format(d.properties.value)); 271 | 272 | //Show the tooltip 273 | d3.select("#tooltip").classed("hidden", false); 274 | }) 275 | .on("mouseout", function() { 276 | 277 | //Hide the tooltip 278 | d3.select("#tooltip").classed("hidden", true); 279 | 280 | }); 281 | 282 | }); 283 | var linear = d3.scaleQuantize() 284 | .domain([ 285 | d3.min(data, function(d) { return d.mean_prob_companies_acquired_by_state; }), 286 | d3.max(data, function(d) { return d.mean_prob_companies_acquired_by_state; }) 287 | ]) 288 | .range(['rgb(215,25,28)','rgb(253,174,97)','rgb(255,255,191)','rgb(166,217,106)','rgb(26,150,65)']); 289 | 290 | var svg = d3.select("svg"); 291 | 292 | svg.append("g") 293 | .attr("class", "legendLinear") 294 | .attr("transform", "translate(1000,200)"); 295 | 296 | var legendLinear = d3.legendColor() 297 | .shape('rect') 298 | .shapeWidth(50) 299 | .shapePadding(5) 300 | .orient('vertical') 301 | .ascending(true) 302 | .scale(linear) 303 | ; 304 | 305 | svg.select(".legendLinear") 306 | .call(legendLinear); 307 | }); 308 | } 309 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Startup-Success-Prediction 2 | 3 | Startups play a major role in economic growth. They bring new ideas, spur innovation, create employment thereby moving the economy. There has been an exponential growth in startups over the past few years. Predicting the success of a startup allows investors to find companies that have the potential for rapid growth, thereby allowing them to be one step ahead of competition. 4 | 5 | ## Motivation 6 | 7 | GMO being an investment management firm is committed to delivering superior investment advice to their clients that are interested in investing in startups. Thus, being able to predict the success of a startup will help GMO to be of greater value to their customers. 8 | 9 | ### Objective 10 | 11 | The objective of the project is to predict whether a startup which is currently operating turn into a success or a failure. The success of a company is defined as the event that gives the company's founders a large sum of money through the process of M&A (Merger and Acquisition) or an IPO (Initial Public Offering). A company would be considered as failed if it had to be shutdown. 12 | 13 | This problem will be solved through a Supervised Machine Learning approach by training a model based on the history of startups which were either acquired or closed. The trained model will then be used to make predictions on startups which are currently operating to determine their success/failure. The final predictions of startup success/failure would then be visualized on a US map by grouping the startup prediction probabilities by state. Thus, states having higher mean startup success probabilities will be have higher percentage of successful startups as compared to states with lower probabilities. Each state will also have a diverging color scale based on their mean startup success probabilities. The user can interactively hover over each state to get additional information about them. 14 | 15 | ## Data 16 | 17 | ### Summary 18 | 19 | GMO has acquired data from another vendor on a trial basis. The data contains industry trends, investment insights and individual company information. Since the data was acquired on a trial basis, it only contains information about companies known till 2012. For this project, only the data of companies operating between 2005 and 2012 has been used. The train/test data contains all companies that were either acquired/closed (used a labels) within that time frame. After training the model, we predict whether startups still operating in the same time frame would be acquired/closed. 20 | 21 | ### Data types 22 | 23 | There are 49 columns out of which 31 will be used as features. The rest provide more information about the data, but will not be used for model training (like company name, company id, latitude, longitude, zip code etc.) 24 | 25 | Some of the top features include: 26 | * age_first_funding_year – quantitative 27 | * age_last_funding_year – quantitative 28 | * relationships – quantitative 29 | * funding_rounds – quantitative 30 | * funding_total_usd – quantitative 31 | * milestones – quantitative 32 | * age_first_milestone_year – quantitative 33 | * age_last_milestone_year – quantitative 34 | * state – categorical 35 | * industry_type – categorical 36 | * has_VC – categorical 37 | * has_angel – categorical 38 | * has_roundA – categorical 39 | * has_roundB – categorical 40 | * has_roundC – categorical 41 | * has_roundD – categorical 42 | * avg_participants – quantitative 43 | * is_top500 – categorical 44 | * status(acquired/closed) – categorical 45 | 46 | ### Data pre-processing 47 | 48 | Data was pre-processed by analyzing the data on mysql database and by joining data from multiple tables. 49 | 50 | ## Task Analysis 51 | 52 | | Index (ID #) | "Domain" Task | Analytic Task (Low-level) | Search Task (Mid-level) | Analyze Task (High-level) | 53 | |:------------:|:--------------:|:-------------:|:-----------:|:------------:| 54 | | 1 | Understand the relationship between different features and labels | Identify | Explore | Discover | 55 | | 2 | Discover best model of predicting startup success | Compare | Explore | Derive | 56 | | 3 | Predict startup success/failure on new data and visualize results | Identify | Browse | Present | 57 | 58 | #### Task #1 59 | 60 | In this visualization, one of the most correlated feature will be selected. It will then be grouped into different ranges and the count of records in each group will be correlated with the labels. At the analytic task, we will be **identifying** the top correlated features and grouping them into different ranges. The user can **explore** the relationship between features in each group to their labels. At the high-level, we are helping users visually **discover** the strong correlation between the feature and labels. 61 | 62 | #### Task #2 63 | 64 | In this step, user will predict best model by **comparing** different machine learning models. He will also have to **explore** different hyper-parameters and then **derive** the final model. 65 | 66 | #### Task #3 67 | 68 | Based on the model the user selects in the previous task, he will then make predictions and **identify** which startups are likely to succeed or fail. The predictions can be grouped by state and visualized at a state level. The use can **browse** through different states to **discover** new insights. 69 | 70 | ## Model Description 71 | 72 | ### Summary of modeling options 73 | 74 | Initially the model can be trained with a basic Decision Tree model. This simple model will give the user a comprehensive analysis of the consequences of each feature, thereby helping him to decide which features should be included to train the final model. Once he gets an idea of how the model performs, the user can then try ensemble models like Random Forests and Gradient Boosting. Random Forests train several trees using a random sample of the data. This helps make the model more robust and less likely to overfit. Gradient Boosting builds trees one at a time by correcting the errors made by the previous trained tree. Hence these models were chosen for model training. 75 | 76 | ### Performance evaluations and selecting the best model 77 | 78 | Each model will be evaluated by plotting ROC curves. Sometimes classification accuracy alone is not enough to decide if the model is good enough, especially in cases where the data can be unbalanced. If most of the data points belong to one specific class, the model can cleverly decide that the best thing to do is to always predict that class and achieve high accuracy. Thus, an ROC Curve which looks at the True Positive Rate and False Positive Rate thereby looking at models based on the balance thresholds of sensitivity and specificity would be a better way of evaluating the model. 79 | 80 | ## Design Process 81 | 82 | ### Initial Sketches and Design Choices 83 | 84 | #### Sketch #1: Scatter Plot and Sankey Diagram for EDA 85 | 86 | This visualization tries to fulfill Task#1, to "Understand the relationship between different features and labels" 87 | 88 | The first visualization is a scatter plot which shows the relationship between the amount spent on acquiring a company to the total funding a company received. The funding amount of a company would be on the x-axis represented in millions of dollars, and the amount spent on acquiring a company on the y-axis represented in billions of dollars. Companies that were not acquired will have an acquisition amount of 0. 89 | 90 | This scatter plot will help visualize the relationship between total funding and price acquired, which will help the end user understand the correlation between the two. 91 | 92 | The visualization to the right uses a sankey diagram to show the correlation between the number of milestones a company had to the company's success/failure. The nodes on the left are the number of milestones a company had which were grouped into different categories, for e.g.: 93 | 94 | * companies with 0 milestones 95 | * companies with 1-5 milestones 96 | * companies with 5-10 milestones 97 | 98 | The nodes on the right represent whether a startup was a success or failure. The links will connect each individual company milestone to its corresponding success/failure. The width of the links are proportional to the flow quantity. 99 | 100 | Sankey diagrams help in locating dominant contributions to an overall flow by putting a visual emphasis on the major flows within a system. This will help the user visualize the correlation between the number of company milestones to its success/failure. 101 | 102 | 103 | 104 | #### Sketch #2: Model selection and hyper-parameter tuning 105 | 106 | This visualization tries to fulfill Task#2, to "Discover best model of predicting startup success". 107 | 108 | Here the end user should be able to train different machine learning models and visualize the model performance through an ROC curve. The user should be able to select different models to train from the dropdown. He can also tweak his model by providing different hyper-parameter values and re-train his model until he is satisfied with the feedback. 109 | 110 | Based on the feedback the user gets from the ROC curve, he can juxtapose several ROC curves with different hyper-parameters and/or different models compare the results to decide upon the best model. The ROC Curve which looks at the True Positive Rate and False Positive Rate thereby looking at models based on the balance thresholds of sensitivity and specificity would be a better way of evaluating the model. 111 | 112 | 113 | 114 | #### Sketch #3: Predictions and visualizations by state 115 | 116 | The next visualization fulfills Task#3, to "Predict startup success/failure on new data and visualize results". 117 | 118 | Once the user has decided upon his final model and hyper-parameters to use, he will then train the model and make predictions on new data. These predictions would decide whether a currently operating startup would be a success or failure. These results would then be grouped by state to get the mean startup success/failure probabilities. These probabilities will then be used to measure the state wise predictions of startup success/failure. Each state will have a diverging color scale based on the mean startup success probability of a state. The user should be able to interactively hover over each state to get additional information about them such as the number of companies predicted to succeed or fail in that state and mean probability of success. 119 | 120 | As geographic location is one of the top factors in predicting startup performance, this visualization would give the end user an idea of how the startups would perform in each state. The user might discover new insights about companies that are performing surprisingly well in certain states, an intuition which they didn't have before. This new perception might give investors more confidence to invest heavily in startups from those states. 121 | 122 | 123 | 124 | Based on these visualizations, we developed the final design sketches as follows: 125 | 126 | ### Final design sketches 127 | 128 | ![slide1](https://raw.githubusercontent.com/RamkishanPanthena/Startup-Success-Prediction/master/sketches/Final%20Sketches/Slide1.JPG) 129 | ![slide2](https://raw.githubusercontent.com/RamkishanPanthena/Startup-Success-Prediction/master/sketches/Final%20Sketches/Slide2.JPG) 130 | ![slide3](https://raw.githubusercontent.com/RamkishanPanthena/Startup-Success-Prediction/master/sketches/Final%20Sketches/Slide3.JPG) 131 | ![slide4](https://raw.githubusercontent.com/RamkishanPanthena/Startup-Success-Prediction/master/sketches/Final%20Sketches/Slide4.JPG) 132 | ![slide5](https://raw.githubusercontent.com/RamkishanPanthena/Startup-Success-Prediction/master/sketches/Final%20Sketches/Slide5.JPG) 133 | 134 | ## Data Analysis: Summary of interesting results 135 | 136 | #### **Visualization #1** 137 | 138 | Correlograms are images of correlation statistics which help us visualize the data in correlation matrices. By visualizing the correlations between features, we can gain intuitions about the high correlations between some variables. We might use these insights to drop a few high correlated features in variable selection of there is no significant loss of information in the model. 139 | 140 | A diverging color palette was used as data from both the positive and negative correlations could be interesting. The positive correlations were represented by blue while the negative correlations were represented by red. From the below Correlogram, we can see that there isn’t a very high correlation between the individual features. Thus, most of the features are unique and can be used for training the model. 141 | 142 | ![corr](https://user-images.githubusercontent.com/29097566/48717478-7f44fb00-ebe7-11e8-8d93-4f2d6ea1c5b2.png) 143 | 144 | #### **Visualization #2** 145 | 146 | Below is a Sankey diagram showing the correlation between the number of company relationships to its success/failure. The nodes on the source end represent the number of relationships a company during its operation which have been grouped into 5 different groups. Color of the node denotes a particular grouping. The value of the node is the sum of incoming or outgoing link values connected to it. These values are visually represented by the nodes height, with longer bars denoting more incoming or outgoing values. The width of the links are directly proportional to the flow quantity. The nodes at the destination represent the labels which tell us whether a company was successful or not and are represented by green and red color respectively. These visualizations have been built in Plotly. The user can run the actual code through a Jupyter notebook and hover the mouse over the nodes/links to determine their strength and the count of incoming of outgoing link values. 147 | 148 | From the below Sankey diagram, we can see that companies with fewer relationships are more likely to fail as compared to companies with more relationships. 149 | 150 | ![sankey1](https://user-images.githubusercontent.com/29097566/50226303-de299b80-0370-11e9-9afa-bc3f3697b8fa.JPG) 151 | 152 | #### **Visualization #3** 153 | 154 | Below is another Sankey diagram which is now showing the correlation between the number of company milestones to its success/failure. The design choices made were similar to the previous Sankey diagram. Here we can see that companies with more number milestones are more likely to succeed as compared to companies with fewer milestones. 155 | 156 | ![sankey2](https://user-images.githubusercontent.com/29097566/50226348-fac5d380-0370-11e9-9c30-1363a4e64a9f.JPG) 157 | 158 | #### **Visualization #4** 159 | 160 | The below visualization is a scatter plot which shows the relationship between the amount spent on acquiring a company to the total funding a company received. The funding amount of a company would be on the x-axis represented in millions of dollars, and the amount spent on acquiring a company on the y-axis represented in billions of dollars. Companies that were not acquired will have an acquisition amount of 0. We can see that there is a positive correlation between the two. 161 | 162 | This visualization has also been built in Plotly. The user can run the actual code through the Jupyter notebook and hover the mouse over each point to interactively compare or show the closet data on hover. 163 | 164 | ![scatter](https://user-images.githubusercontent.com/29097566/48718553-9f75b980-ebe9-11e8-95b8-d1449da43173.png) 165 | 166 | ## Final Visualization 167 | 168 | This project implements a web-based client-side visualization connected via a REST API to server-side modeling which is running on your own server. The final visualization has been built in HTML, CSS, D3 and Flask(Python). Models have been trained in Python and deployed through a heroku app. You will need to install the following Python packages first: 169 | 170 | * pandas 171 | * sklearn 172 | * numpy 173 | * flask, flask_cors 174 | * simplejson 175 | * Werkzeug 176 | * csv 177 | 178 | The heroku app has been deployed at: 179 | http://startup-success-prediction-new.herokuapp.com/ 180 | 181 | The final visualization uses a diverging color scale showing the state-wise success probability of startups. Green and Red colors were consistently used through the project with green being used for states/companies with higher success probabilities and red used for states/companies with lower success probabilities. States like Alaska, North Dakota where no startups have been founded have been greyed out. The final visualization also has a legend showing probability classes for different color schemes. The user can also gain additional information like the number of startups predicted to be successful or unsuccessful at a state level. He can also find out what is the mean startup success probability in that state by hovering over any state. 182 | 183 | Finally we have an app with the following functionalities: 184 | 185 | 1. Train from a dropdown of different machine learning models and their corresponding hyper-parameters 186 | 2. Visualize the performance of each model through an ROC Curve 187 | 3. Juxtapose several ROC Curves to contrast the performance of different models and decide upon the best model 188 | 4. Make predictions on new data 189 | 5. Visualize these predictions at a state level 190 | 6. Add interactively to allow the user to gain additional information about companies in each state 191 | 192 | Scope for future: 193 | 194 | 1. Provide more machine learning models for model training 195 | 2. Allow the user to zoom in on any state and interactively visualize the region-wise performance of startups in that state using the company's latitude and longitude coordinates 196 | 197 | ### UI Walkthrough 198 | 199 | Below is an embedded youtube video giving a walkthrough of the UI: 200 | 201 | [![Alt text](https://img.youtube.com/vi/cTs7wR2H8cg/0.jpg)](https://www.youtube.com/watch?v=cTs7wR2H8cg) 202 | 203 | ## Conclusion 204 | 205 | We were able to successfully build a machine learning model that predicts the success/failure of a startup. We were also able to implement a web-based client-side visualization connected via a REST API to server-side modeling. We were also able to generate interesting visualizations through preliminary insights and plots. 206 | 207 | For future improvements, we could work on extracting more features like evaluate startup presence on the web, number of unique domains mentioning the startup, burn rate etc. Using these features, one could get more ideas on creating more interactive web-based visualizations. 208 | 209 | -------------------------------------------------------------------------------- /d3/data/funding-data.csv: -------------------------------------------------------------------------------- 1 | id,acquiring_company_name,acquiring_object_id,acquired_company_name,acquired_object_id,price_amount,funding_total_usd,status 2 | 1,Facebook,c:5,FriendFeed,c:1001,47500000,5000000,acquired 3 | 5,,,"ITOG, Inc.",c:10153,0,205000,closed 4 | 6,AOL,c:351,StudioNow,c:10158,36500000,15286415,acquired 5 | 7,Microsoft,c:1242,Yammer,c:10176,1200000000,142000000,acquired 6 | 9,Cisco,c:28,PostPath,c:10197,215000000,24300000,acquired 7 | 13,,,PlayerDuel,c:103204,0,21189,closed 8 | 15,,,Picateers,c:10398,0,12100000,closed 9 | 19,AOL,c:351,Surphace,c:105,25000000,3500000,acquired 10 | 20,Iron Mountain,c:21154,Mimosa Systems,c:10512,112000000,53816776,acquired 11 | 22,Vocus,c:4687,iContact,c:106,169000000,53350000,acquired 12 | 23,,,Geodelic Systems,c:10610,0,10500000,closed 13 | 25,Shanda Games,c:34079,Mochi Media,c:1069,80000000,14000000,acquired 14 | 27,,,Pixelpipe,c:10751,0,2300000,closed 15 | 29,Monsanto,c:29610,The Climate Corporation,c:1078,930000000,108900000,acquired 16 | 30,GSI Commerce,c:28712,FetchBack,c:10864,40000000,1000000,acquired 17 | 31,Yahoo!,c:29,Tumblr,c:1088,1100000000,125250000,acquired 18 | 33,,,ConSentry Networks,c:10913,0,46400000,closed 19 | 34,TIBCO Software,c:4967,LogLogic,c:10915,130000000,8800000,acquired 20 | 35,,,Semantra,c:10969,0,9100000,closed 21 | 39,,,Buzzwire,c:1120,0,12000000,closed 22 | 42,,,B-Side Entertainment,c:11397,0,7350000,closed 23 | 45,,,Predictify,c:1148,0,4300000,closed 24 | 46,,,WeAre.Us,c:11498,0,50000,closed 25 | 50,,,MyShape,c:11817,0,28000000,closed 26 | 51,NETGEAR,c:16675,Avaak,c:11888,31000000,17000000,acquired 27 | 52,,,Songbird,c:1205,0,11015719,closed 28 | 53,Amazon,c:317,Kiva Systems,c:12052,775000000,11600000,acquired 29 | 55,,,SolFocus,c:12099,0,211403000,closed 30 | 56,,,GridNetworks,c:1212,0,30000000,closed 31 | 57,AOL,c:351,Quigo,c:1214,363000000,35000000,acquired 32 | 58,IBM,c:3246,Guardium,c:12171,225000000,11800000,acquired 33 | 60,Salesforce,c:1972,ExactTarget,c:12178,2500000000,238209999,acquired 34 | 61,,,Vontoo,c:12179,0,2192000,closed 35 | 62,,,DubMeNow,c:12224,0,7516703,closed 36 | 64,DG,c:13228,Peer39,c:1224,15500000,29915001,acquired 37 | 65,McAfee,c:14236,MX Logic,c:12285,140000000,4000000,acquired 38 | 66,Oracle Corporation,c:2680,Vitrue,c:1234,300000000,33000000,acquired 39 | 67,Google,c:59,"Wildfire, a division of Google",c:12348,350000000,14100000,acquired 40 | 70,,,Faves,c:1253,0,1500000,closed 41 | 71,,,Goodmail Systems,c:12630,0,45000000,closed 42 | 73,IBM,c:3246,Trusteer,c:12723,800000000,10100000,acquired 43 | 74,Microsoft,c:1242,Farecast,c:128,115000000,20600000,acquired 44 | 75,Reply! Inc.,c:1316,MerchantCircle,c:1283,60000000,14300000,acquired 45 | 76,,,Minekey,c:1284,0,3600000,closed 46 | 77,Taylor Nelson Sofres,c:3063,Compete,c:1285,150000000,33000000,acquired 47 | 80,,,Newmerix,c:13006,0,7000000,closed 48 | 82,Google,c:59,DocVerse,c:13046,25000000,1300000,acquired 49 | 83,Green Dot Corporation,c:35247,Loopt,c:1309,43000000,32006000,acquired 50 | 85,,,CV Ingenuity,c:131583,0,30926154,closed 51 | 86,Monster,c:1735,Trovix,c:13168,72500000,18250000,acquired 52 | 88,,,GetBack,c:13277,0,1700000,closed 53 | 89,,,Q-magic,c:13358,0,650000,closed 54 | 91,,,GlobalCrypto,c:13410,0,1000000,closed 55 | 93,LiveUniverse,c:2358,Pageflakes,c:135,3000000,4100000,acquired 56 | 95,,,Planet Metrics,c:13504,0,2300000,closed 57 | 97,,,Azelon Pharmaceuticals,c:137274,0,6922000,closed 58 | 99,,,PublicEarth,c:13733,0,4000000,closed 59 | 103,FTI Consulting,c:26482,Attenex,c:13874,88000000,5000000,acquired 60 | 105,AOL,c:351,The Huffington Post,c:1388,315000000,37000000,acquired 61 | 106,,,NebuAd,c:1389,0,30150000,closed 62 | 107,,,Swivel,c:139,0,2000000,closed 63 | 108,,,Amadesa,c:13921,0,12800000,closed 64 | 109,,,MobileReactor,c:139729,0,1175000,closed 65 | 111,,,AutoVirt,c:14028,0,24300000,closed 66 | 112,Tellabs,c:34407,WiChorus,c:14036,165000000,42500000,acquired 67 | 114,AOL,c:351,Bebo,c:142,850000000,15000000,acquired 68 | 114,Reachd,c:5602,Bebo,c:142,10000000,15000000,acquired 69 | 115,,,Peek & Co,c:14345,0,626096,closed 70 | 117,,,xkoto,c:1445,0,11035000,closed 71 | 118,,,Press,c:144611,0,70000,closed 72 | 119,,,VeteranCentral.com,c:144631,0,150000,closed 73 | 120,,,DateMyFamily.com,c:144687,0,100000,closed 74 | 121,Equifax,c:2823,TrustedID,c:1447,30000000,25000000,acquired 75 | 122,,,CakeStyle,c:144825,0,1000000,closed 76 | 123,,,Hit Systems,c:14483,0,125000,closed 77 | 125,FIS,c:68232,mFoundry,c:14608,120000000,40300000,acquired 78 | 127,,,AdexLink,c:146596,0,2000000,closed 79 | 128,,,REMOTV,c:14666,0,2700000,closed 80 | 132,,,Classiphix,c:148284,0,15000,closed 81 | 133,Symantec,c:3320,Vontu,c:14854,350000000,20000000,acquired 82 | 134,,,"MoveableCode, Inc.",c:148566,0,350000,closed 83 | 141,WhitePages.com,c:2345,Snapvine,c:1499,10000000,12000000,acquired 84 | 142,,,Exit41,c:14993,0,24200000,closed 85 | 145,,,StyleZen,c:150577,0,20000,closed 86 | 148,blinkx,c:108,Rhythm NewMedia,c:15123,65000000,28932322,acquired 87 | 149,,,Zentact,c:15129,0,250000,closed 88 | 150,,,Utterz,c:1518,0,4000000,closed 89 | 151,,,Wantful,c:152560,0,5500000,closed 90 | 152,KEYW Corporation,c:40595,SenSage,c:15294,24000000,37079000,acquired 91 | 153,,,Gaston Labs,c:153403,0,100000,closed 92 | 154,Yahoo!,c:29,Dapper,c:1541,55000000,3000000,acquired 93 | 155,,,eBillme,c:15458,0,23000000,closed 94 | 157,Google,c:59,FeedBurner,c:155,100000000,8000000,acquired 95 | 158,,,Rudder,c:15509,0,2000000,closed 96 | 160,,,Friendsignia,c:155621,0,15000,closed 97 | 161,,,Nalace Corporation,c:155821,0,19000,closed 98 | 162,,,mBeat Media,c:15599,0,1500000,closed 99 | 165,,,SeeSaw Networks,c:15687,0,21138876,closed 100 | 167,,,Twitpay,c:15704,0,500000,closed 101 | 168,,,Robotgalaxy,c:15709,0,12000000,closed 102 | 169,,,Soshowise,c:157234,0,50000,closed 103 | 172,Western Digital,c:22518,Virident Systems,c:15829,685000000,129677153,acquired 104 | 173,,,Newfield Design,c:158294,0,662000,closed 105 | 175,"Verizon Communications, Inc.",c:4843,EdgeCast Networks,c:1587,350000000,74000000,acquired 106 | 176,,,Avega Systems,c:15870,0,9500000,closed 107 | 177,,,Hammerhead Systems,c:15887,0,48000000,closed 108 | 178,,,Neopolitan Networks,c:15888,0,3170000,closed 109 | 179,Incredimail,c:13906,Smilebox,c:1590,40000000,14000000,acquired 110 | 180,eBay,c:20,Bill Me Later,c:1595,945000000,100000000,acquired 111 | 181,ViaSat,c:33731,WildBlue,c:15996,568000000,50000000,acquired 112 | 182,Walmart,c:29025,Kosmix,c:1600,300000000,55000000,acquired 113 | 184,,,VoloMedia,c:16066,0,12000000,closed 114 | 185,Twitter,c:12,Summize,c:1607,15000000,750000,acquired 115 | 187,,,Applied Identity,c:16111,0,33000000,closed 116 | 188,Amazon,c:317,Goodreads,c:1612,150000000,2750000,acquired 117 | 189,British Telecom,c:4196,Ribbit,c:1613,105000000,11000000,acquired 118 | 190,,,Kickfire,c:16134,0,20000000,closed 119 | 192,Hewlett-Packard,c:355,ArcSight,c:16178,1500000000,26914000,acquired 120 | 193,,,AdYouNet,c:16179,0,1400000,closed 121 | 194,LSI,c:5722,ONStor,c:16181,25000000,65800000,acquired 122 | 195,Broadcom,c:17124,Provigent,c:16189,313000000,32000000,acquired 123 | 198,,,Contur,c:162219,0,20000,closed 124 | 199,,,Aquest Systems,c:16264,0,20000000,closed 125 | 200,,,WeShow,c:1627,0,5000000,closed 126 | 201,Spirent Communications,c:65077,Mu Dynamics,c:16271,40000000,24000000,acquired 127 | 203,Intel,c:71,Sensory Networks,c:16276,20000000,17400000,acquired 128 | 204,Skype,c:62,Qik,c:1628,150000000,14800000,acquired 129 | 212,,,RipCode,c:1637,0,33000000,closed 130 | 214,,,"RiverGlass, Inc.",c:16406,0,6400000,closed 131 | 216,,,Digital Performance,c:16488,0,4476171,closed 132 | 217,,,Turf Geography Club,c:164922,0,890000,closed 133 | 222,,,Rapport,c:16607,0,25500000,closed 134 | 224,Tessera,c:9514,Siimpel Corporation,c:16613,15000000,53818873,acquired 135 | 225,Vonage,c:7618,Vocalocity,c:16618,130000000,13600000,acquired 136 | 226,,,27 Perry,c:166763,0,118000,closed 137 | 228,Zynga,c:1812,Challenge Games,c:1669,20500000,14500000,acquired 138 | 229,,,Sway,c:167,0,2000000,closed 139 | 230,,,Focus,c:16709,0,12170000,closed 140 | 232,SurveyMonkey,c:21875,Infinity Box,c:1674,35000000,118000,acquired 141 | 233,,,Stiki Digital,c:16753,0,325000,closed 142 | 234,,,Extreme Enterprises,c:16757,0,1455000,closed 143 | 235,,,scanR,c:16761,0,14200000,closed 144 | 236,,,QSecure,c:16770,0,49000000,closed 145 | 237,,,spotdock,c:167795,0,600000,closed 146 | 239,Broadcom,c:17124,BroadLight,c:16874,230000000,27000000,acquired 147 | 242,,,SimulScribe,c:1692,0,5700000,closed 148 | 245,,,IonLogix Systems,c:16973,0,30000,closed 149 | 247,,,SendMeHome.com,c:17030,0,50000,closed 150 | 254,,,D2C Games,c:17252,0,7500000,closed 151 | 257,SMSC,c:34751,Symwave,c:17282,8300000,26200000,acquired 152 | 258,Google,c:59,Meebo,c:173,100000000,70000000,acquired 153 | 259,Cisco,c:28,Meraki,c:1731,1200000000,80000000,acquired 154 | 260,,,Relationship Analytics,c:173118,0,125000,closed 155 | 262,,,Speedshape,c:17429,0,5000000,closed 156 | 265,,,Power Analog Microelectronics,c:17481,0,12000000,closed 157 | 266,,,Shocking Technologies,c:17501,0,55900000,closed 158 | 268,,,Appscio,c:17512,0,7000000,closed 159 | 269,,,bluepulse,c:1753,0,6000000,closed 160 | 272,IBM,c:3246,Storwize,c:17581,140000000,28000000,acquired 161 | 274,,,Aurora Feint,c:17610,0,7660000,closed 162 | 277,SGI,c:12011,Copan Systems,c:17633,2000000,68400000,acquired 163 | 278,Imation,c:70298,Nexsan,c:17634,120000000,24500000,acquired 164 | 279,Synaptics,c:17655,Validity Sensors,c:17656,255000000,82600000,acquired 165 | 280,,,ARPU,c:1766,0,15564591,closed 166 | 281,Qualcomm,c:323,Firethorn,c:17660,210000000,25131611,acquired 167 | 287,,,OpVista,c:17775,0,43000000,closed 168 | 288,Riverbed Technology,c:17731,Mazu Networks,c:17795,47000000,7000000,acquired 169 | 290,,,Hidden City Games,c:17857,0,15000000,closed 170 | 292,LivePerson,c:7416,NuConomy,c:1789,3000000,3300000,acquired 171 | 295,EMC,c:966,ScaleIO,c:179507,200000000,12000000,acquired 172 | 296,,,Conformity,c:17974,0,3000000,closed 173 | 298,,,CypherMax,c:18027,0,148000000,closed 174 | 301,,,Etology,c:18080,0,4000000,closed 175 | 303,BroadSoft,c:3407,Casabi,c:18163,1950000,12800000,acquired 176 | 304,Google,c:59,Invite Media,c:1832,80000000,5000000,acquired 177 | 305,MySpace,c:494,iLike,c:184,20000000,16500000,acquired 178 | 308,SingTel,c:16041,Amobee,c:1870,321000000,72000000,acquired 179 | 312,,,Punch Entertainment,c:18897,0,1200000,closed 180 | 313,,,Current Media,c:1895,0,15000000,closed 181 | 315,,,MOLI,c:1899,0,55600000,closed 182 | 317,,,Pocket Communications Northeast,c:19101,0,125000000,closed 183 | 320,SanDisk,c:3275,Pliant Technology,c:19234,327000000,50300006,acquired 184 | 324,,,walkby,c:195311,0,650000,closed 185 | 325,,,Kashless,c:19538,0,9000000,closed 186 | 326,,,Sharetivity,c:1957,0,2000000,closed 187 | 329,IXIA,c:26797,BreakingPoint Systems,c:1967,160000000,20000000,acquired 188 | 334,,,Pacinian,c:19869,0,350000,closed 189 | 336,,,PayParade Pictures,c:20033,0,200000,closed 190 | 340,,,Hotelicopter,c:202,0,5000000,closed 191 | 341,TeleCommunication Systems,c:26802,Networks in Motion,c:20253,170000000,10000000,acquired 192 | 345,Medtronic,c:33654,Ardian,c:20506,800000000,64080000,acquired 193 | 347,DG,c:13228,Republic Project,c:20672,1400000,1000000,acquired 194 | 349,,,LookStat,c:20832,0,500000,closed 195 | 350,CA Technologies,c:26050,Nimsoft,c:20847,350000000,22300000,acquired 196 | 354,,,Jobzle,c:21359,0,30000,closed 197 | 356,,,Airship Ventures,c:21492,0,10500000,closed 198 | 361,Brightcove,c:325,Zencoder,c:21762,30000000,2000000,acquired 199 | 362,,,What They Like,c:2178,0,990000,closed 200 | 363,Sanofi Aventis,c:28373,BiPar Sciences,c:21940,500000000,73000000,acquired 201 | 366,LBI International,c:4010,Mr. Youth,c:22057,50000000,15100000,acquired 202 | 367,,,Factery,c:22113,0,1200000,closed 203 | 369,eBay,c:20,Milo,c:22203,75000000,4950000,acquired 204 | 370,Cisco,c:28,Metreos Corporation,c:22291,28000000,4355000,acquired 205 | 372,,,Fetch MD,c:223501,0,25000,closed 206 | 373,,,Advaliant,c:2245,0,100000,closed 207 | 375,,,Smalltown,c:2272,0,4000000,closed 208 | 376,SuccessFactors,c:1790,CubeTree,c:22721,50000000,11500000,acquired 209 | 378,eBay,c:20,Where,c:2275,135000000,19100000,acquired 210 | 380,,,Matisse Networks,c:22898,0,7500000,closed 211 | 382,Nordstrom,c:29929,HauteLook,c:23052,270000000,41000000,acquired 212 | 384,Synchronoss Technologies,c:28415,Miyowa,c:23098,59000000,11725600,acquired 213 | 386,The Walt Disney Company,c:342,Playdom,c:23125,763200000,76000000,acquired 214 | 390,,,Canopy Financial,c:23192,0,89500000,closed 215 | 391,,,Elanti Systems,c:23234,0,5500000,closed 216 | 392,,,Wi-Chi,c:23251,0,7000000,closed 217 | 393,,,Social Collective,c:23335,0,20000,closed 218 | 398,Apple,c:1654,Topsy Labs,c:23588,200000000,35200000,acquired 219 | 399,,,Ecast,c:23692,0,30300000,closed 220 | 401,,,ZettaCore,c:23788,0,21000000,closed 221 | 403,Emcore,c:72249,Soliant Energy,c:23903,450000,18000000,acquired 222 | 406,,,DynaPump,c:24040,0,6000000,closed 223 | 407,,,Apieron,c:24206,0,43500000,closed 224 | 409,,,Anaphore,c:24281,0,24300000,closed 225 | 411,Oversee,c:1809,SnapNames,c:2434,35000000,8800000,acquired 226 | 413,,,Biofisica,c:24570,0,7300000,closed 227 | 414,Tremor Video,c:104377,ScanScout,c:246,86000000,17500000,acquired 228 | 415,,,Limerick BioPharma,c:24681,0,49008830,closed 229 | 416,Eli Lilly,c:31588,Avid Radiopharmaceuticals,c:24727,800000000,69900000,acquired 230 | 417,,,JackPot Rewards,c:2478,0,16700000,closed 231 | 418,,,GenieTown,c:2481,0,2000000,closed 232 | 419,SingTel,c:16041,Pixable,c:24853,26500000,6100000,acquired 233 | 421,Teleflex,c:65984,Semprus BioSciences,c:24896,30000000,27000000,acquired 234 | 423,,,FameCast,c:2497,0,9700000,closed 235 | 425,Johnson & Johnson,c:12137,Aragon Pharmaceuticals,c:25001,1000000000,122000000,acquired 236 | 431,Alexion Pharmaceuticals,c:67598,Taligen Therapeutics,c:25324,111000000,78750000,acquired 237 | 434,,,Biolex Therapeutics,c:25383,0,162264126,closed 238 | 435,,,PhotoThera,c:25449,0,80500000,closed 239 | 439,,,Azingo,c:25688,0,80000000,closed 240 | 441,AstraZeneca,c:13450,Pearl Therapeutics,c:25737,1150000000,232000100,acquired 241 | 443,,,Broadway Networks,c:25756,0,954000,closed 242 | 444,CA Technologies,c:26050,Layer 7 Technologies,c:25782,155000000,19900000,acquired 243 | 445,,,TwtBks,c:25808,0,25000,closed 244 | 447,Shutterfly,c:1438,Tiny Pictures,c:2593,2600000,11200000,acquired 245 | 448,,,Kurani Interactive,c:25997,0,275000,closed 246 | 455,,,Bling Nation,c:26368,0,28000000,closed 247 | 456,,,Revetto,c:26390,0,20000,closed 248 | 457,dunnhumby,c:39178,BzzAgent,c:26460,60000000,14500000,acquired 249 | 458,EMC,c:966,Kazeon,c:2647,150000000,41000000,acquired 250 | 459,Broadcom,c:17124,Beceem Communications,c:26556,316000000,69600000,acquired 251 | 460,,,Nordic Windpower,c:26612,0,54000000,closed 252 | 462,LSI,c:5722,SandForce,c:26685,400000000,46000000,acquired 253 | 463,Bakbone Software,c:26703,Asempra Technologies,c:26702,1850000,20000000,acquired 254 | 465,Bakbone Software,c:26703,ColdSpark,c:26806,15900000,6500000,acquired 255 | 466,,,Nethra Imaging,c:26813,0,14000000,closed 256 | 467,,,Advanced Marketing & Media Group,c:26863,0,500000,closed 257 | 469,Comcast,c:4175,Plaxo,c:27,150000000,28300000,acquired 258 | 473,Abbott Labs,c:10475,OptiMedica,c:27142,400000000,51000000,acquired 259 | 474,,,Advanced Power Projects,c:27159,0,13000000,closed 260 | 475,Lightera,c:26738,Luminus Devices,c:27163,22000000,135826373,acquired 261 | 476,Mellanox Technologies,c:62791,KOTURA,c:27285,82000000,27690547,acquired 262 | 477,Yahoo!,c:29,Right Media,c:2731,850000000,45000000,acquired 263 | 480,,,Plato Networks,c:27439,0,27500000,closed 264 | 485,,,Texert,c:27740,0,3000000,closed 265 | 486,,,eMinor,c:27741,0,5000000,closed 266 | 487,Dialog Semiconductor,c:33565,iWatt,c:27779,345000000,19400000,acquired 267 | 488,,,DeliveryEdge,c:278,0,1500000,closed 268 | 489,,,Correlix,c:27938,0,11400000,closed 269 | 492,,,Yub,c:280611,0,12000000,closed 270 | 494,,,PeerMe,c:281,0,5000000,closed 271 | 495,,,VT Silicon,c:28114,0,8800000,closed 272 | 496,,,lifeIO,c:28162,0,500000,closed 273 | 498,,,Rayspan,c:28307,0,12500000,closed 274 | 500,,,HighlightCam,c:28403,0,3000000,closed 275 | 501,VMware,c:5990,Blue Lane Technologies,c:28420,15000000,13400000,acquired 276 | 502,,,Danotek Motion Technologies,c:28424,0,42700000,closed 277 | 506,CommScope,c:12394,Redwood Systems,c:28482,60000000,34250000,acquired 278 | 506,CommScope,c:12394,Redwood Systems,c:28482,60000000,34250000,acquired 279 | 507,CommScope,c:12394,Redwood Systems,c:28482,60000000,34250000,acquired 280 | 507,CommScope,c:12394,Redwood Systems,c:28482,60000000,34250000,acquired 281 | 511,McAfee,c:14236,Lockdown Networks,c:28768,2000000,8600000,acquired 282 | 512,Ancestry,c:1071,Footnote,c:2889,28000000,10000000,acquired 283 | 513,Verint Systems,c:27810,Vovici,c:2902,56500000,16300000,acquired 284 | 519,Kofax,c:29411,Kapow Software,c:2953,47500000,19743770,acquired 285 | 521,,,Zoodak,c:29604,0,580000,closed 286 | 522,,,Three Screen Games,c:29605,0,500000,closed 287 | 523,Yahoo!,c:29,Rockmelt,c:29618,65000000,39875000,acquired 288 | 526,,,ScribeStorm,c:2993,0,225000,closed 289 | 527,Microsoft,c:1242,Powerset,c:30,100000000,22500000,acquired 290 | 528,,,Ubidyne,c:30041,0,58500000,closed 291 | 529,International Rectifier,c:70064,CHiL Semiconductor,c:30152,75000000,22487000,acquired 292 | 530,,,UpMo,c:30173,0,3700000,closed 293 | 531,,,SearchMe,c:3027,0,43600000,closed 294 | 532,,,Resonant Vibes,c:30290,0,350000,closed 295 | 533,,,Smith & Tinker,c:30307,0,29000000,closed 296 | 536,Cisco,c:28,Pure Networks,c:30526,120000000,12500000,acquired 297 | 537,Ceradyne,c:30528,SemEquip,c:30527,125000000,26000000,acquired 298 | 538,Nuance Communications,c:5473,SNAPin Software,c:30537,204260000,22300000,acquired 299 | 539,,,Solvate,c:30562,0,10300000,closed 300 | 540,,,Impeva,c:30667,0,10000000,closed 301 | 542,HSW International,c:14745,DailyStrength,c:3080,3125000,5000000,acquired 302 | 548,,,Glacier Bay,c:31120,0,28000000,closed 303 | 549,,,Queplix,c:31141,0,1500000,closed 304 | 551,,,Bueda,c:31294,0,125000,closed 305 | 554,,,Natural Cleaners Colorado,c:31452,0,1900000,closed 306 | 555,,,Roxro Pharma,c:31488,0,42700000,closed 307 | 556,,,AccelGolf,c:31514,0,457282,closed 308 | 557,,,Satiety,c:31546,0,62800000,closed 309 | 558,,,Paracor Medical,c:31549,0,44000000,closed 310 | 559,Thermage,c:148832,Reliant Technologies,c:31559,66200000,15000000,acquired 311 | 560,Ipsen,c:31615,Tercica,c:31614,480300000,83000000,acquired 312 | 561,Allergan,c:41450,MAP Pharmaceuticals,c:31754,958000000,50000000,acquired 313 | 562,,,Oorja Protonics,c:31764,0,21470000,closed 314 | 563,,,Elastra,c:3178,0,14600000,closed 315 | 565,Local.com,c:25597,Krillion,c:3180,3500000,9150000,acquired 316 | 566,Amgen,c:23339,Avidia,c:31832,380000000,72300000,acquired 317 | 567,,,kwiry,c:3185,0,1000000,closed 318 | 569,Thermo Fisher Scientific,c:31663,Ahura Scientific,c:31866,145000000,17000000,acquired 319 | 571,VMware,c:5990,Digital Fuel,c:31888,85000000,5000000,acquired 320 | 572,,,Dyyno,c:31909,0,14078664,closed 321 | 575,,,Origen Therapeutics,c:31968,0,2000000,closed 322 | 578,,,Authenticlick,c:3202,0,5000000,closed 323 | 579,Asterand,c:40135,BioSeek,c:32023,14000000,10000000,acquired 324 | 582,,,Idooble,c:32144,0,400000,closed 325 | 585,Google,c:59,Waze,c:32475,1100000000,67000000,acquired 326 | 586,,,Tapioca Mobile,c:3267,0,5000000,closed 327 | 587,,,YESTODATE.COM,c:32686,0,1000000,closed 328 | 588,,,WebVisible,c:3288,0,37000000,closed 329 | 589,,,HX Diagnostics,c:32977,0,7800000,closed 330 | 591,,,TalkPlus,c:334,0,5500000,closed 331 | 592,Twitter,c:12,Bluefin Labs,c:33416,80000000,20350000,acquired 332 | 593,Walmart,c:29025,Vudu,c:335,100000000,36000000,acquired 333 | 595,,,RFinity,c:33519,0,4000000,closed 334 | 596,,,Attune Systems,c:33771,0,18000000,closed 335 | 599,,,Transactiv,c:33912,0,1200000,closed 336 | 600,Salesforce,c:1972,Desk,c:33949,50000000,5700000,acquired 337 | 602,,,LikeList,c:34028,0,5000000,closed 338 | 603,Salesforce,c:1972,Dimdim,c:3413,31000000,8400000,acquired 339 | 605,,,Chronicity,c:34267,0,19550000,closed 340 | 606,,,Zipano,c:34338,0,25000,closed 341 | 607,,,PermissionTV,c:344,0,9000000,closed 342 | 608,,,Innovative Silicon,c:34401,0,72000000,closed 343 | 610,,,Hotlist,c:34522,0,1449998,closed 344 | 611,Brand Networks,c:153484,Optimal,c:34545,35000000,7600000,acquired 345 | 612,Facebook,c:5,Hot Potato,c:34549,10000000,1420000,acquired 346 | 613,Cisco,c:28,ScanSafe,c:34618,183000000,28500000,acquired 347 | 615,888 Holdings,c:24216,Mytopia,c:3487,18000000,3500000,acquired 348 | 616,,,Workstreamer,c:3505,0,3500000,closed 349 | 617,,,Devtap,c:35148,0,2600000,closed 350 | 618,,,Viacor,c:35157,0,19009671,closed 351 | 620,,,RPO,c:35402,0,44000000,closed 352 | 621,,,Trinity Biosystems,c:35433,0,28500000,closed 353 | 622,,,Teranode,c:35475,0,10400000,closed 354 | 623,Logitech,c:2989,"LifeSize, a Division of Logitech",c:35511,405000000,42500000,acquired 355 | 625,,,Etelos,c:3562,0,4087500,closed 356 | 628,Broadcom,c:17124,Teknovus,c:35887,123000000,62600000,acquired 357 | 629,,,JavaJobs,c:35903,0,50000,closed 358 | 630,Symantec,c:3320,Clearwell Systems,c:3610,410000000,29000000,acquired 359 | 632,,,Storspeed,c:36192,0,13000000,closed 360 | 633,,,NewsBasis,c:36276,0,545000,closed 361 | 636,,,Nirvanix,c:365,0,70000000,closed 362 | 637,,,SemiSouth Laboratories,c:36579,0,39000000,closed 363 | 638,,,Conecta 2,c:367,0,15000,closed 364 | 639,,,ThermalTherapeuticSystems,c:36742,0,3508462,closed 365 | 640,Real Networks,c:845,Game Trust,c:3686,20000000,9000000,acquired 366 | 641,,,Blade Games World,c:36868,0,5926462,closed 367 | 642,,,Apnex Medical,c:36892,0,44000000,closed 368 | 644,Global Crossing,c:10241,Genesis Networks,c:36936,27000000,22165091,acquired 369 | 645,,,itzbig,c:3717,0,4000000,closed 370 | 646,,,Five Star Technologies,c:37173,0,13222860,closed 371 | 647,HTC Corp,c:3132,Dashwire,c:37215,18500000,5056559,acquired 372 | 651,Braintree,c:46943,Venmo,c:37635,26200000,1200000,acquired 373 | 652,,,NextIO,c:37647,0,49600000,closed 374 | 653,Yahoo!,c:29,Xoopit,c:3770,20000000,6500000,acquired 375 | 654,PLX Technology,c:58544,Teranetics,c:37712,53800000,52800000,acquired 376 | 655,,,Zeo,c:37752,0,26138500,closed 377 | 657,,,LegiTime Technologies,c:37780,0,4000000,closed 378 | 660,NetApp,c:1727,Akorri Networks,c:37929,60000000,48730000,acquired 379 | 661,,,Kardia Health Systems,c:37934,0,18400000,closed 380 | 663,,,Thwapr,c:38019,0,2000000,closed 381 | 668,,,Digit Wireless,c:38273,0,10000000,closed 382 | 670,AOL,c:351,about.me,c:38337,35000000,5675000,acquired 383 | 671,Experian,c:2175,41st Parameter,c:38338,324000000,38064570,acquired 384 | 673,,,EvoApp,c:38460,0,3352194,closed 385 | 674,,,iosil Energy,c:38504,0,13875001,closed 386 | 675,,,iGroup Network,c:38590,0,2240000,closed 387 | 676,,,Make It Work,c:38655,0,1000000,closed 388 | 678,,,Hello Chair,c:38794,0,889999,closed 389 | 679,,,Jodange,c:3902,0,1200000,closed 390 | 680,Sony,c:304,Gaikai,c:39101,380000000,45039999,acquired 391 | 681,,,iKoa,c:39105,0,18700000,closed 392 | 683,Dot Hill Systems,c:39285,Cloverleaf Communications,c:39283,12000000,11000000,acquired 393 | 684,Eisai,c:39681,AkaRx,c:39364,255000000,26100000,acquired 394 | 685,,,Sweepery,c:39399,0,500000,closed 395 | 686,eBay,c:20,Zong,c:3949,240000000,27500000,acquired 396 | 687,Akamai Technologies,c:424,Cotendo,c:3950,268000000,36000000,acquired 397 | 688,,,Zymetis,c:39507,0,2257464,closed 398 | 689,VMware,c:5990,Nicira Networks,c:39521,1260000000,41815432,acquired 399 | 691,,,Helix Therapeutics,c:39596,0,3100000,closed 400 | 692,Symantec,c:3320,GuardianEdge Technologies,c:3962,70000000,19500000,acquired 401 | 693,,,Spectrawatt,c:39661,0,41400000,closed 402 | 694,,,MPOWER Mobile,c:39755,0,5000000,closed 403 | 695,,,Storm Exchange,c:39801,0,10800000,closed 404 | 698,Cisco,c:28,Pure Digital Technologies,c:3991,590000000,68200000,acquired 405 | 699,betaworks,c:20953,Digg,c:4,500000,45000000,acquired 406 | 701,Silicon Image,c:15297,SiBEAM,c:4014,25500000,113750000,acquired 407 | 702,,,C2 Microsystems,c:40147,0,10000000,closed 408 | 703,,,EmSense,c:40152,0,13000000,closed 409 | 704,,,DNP Green Technology,c:40197,0,12000000,closed 410 | 706,Sunovion Pharmaceuticals,c:85949,Elevation Pharmaceuticals,c:40417,430000000,89400001,acquired 411 | 708,,,Media Chaperone,c:40665,0,1050000,closed 412 | 709,Sourcefire,c:19066,Immunet Corporation,c:40762,21000000,2000000,acquired 413 | 717,,,MogoTix,c:41334,0,115000,closed 414 | 718,,,Taggstr,c:41409,0,50000,closed 415 | 722,,,Reef Point Systems,c:41747,0,52000000,closed 416 | 724,,,Verari Systems,c:41968,0,20000000,closed 417 | 725,,,Andigilog,c:42062,0,18000000,closed 418 | 727,,,Moonshoot,c:42112,0,6760000,closed 419 | 729,,,Certeon,c:42163,0,19000000,closed 420 | 731,Outerwall,c:64747,ecoATM,c:42223,350000000,71400000,acquired 421 | 732,,,WiLinx,c:42227,0,15000000,closed 422 | 733,,,Berggi,c:42441,0,12000000,closed 423 | 734,,,PeerPong,c:42496,0,2800000,closed 424 | 737,,,Internet Pawn,c:42519,0,7228704,closed 425 | 738,,,Qlusters,c:42610,0,10360000,closed 426 | 739,McAfee,c:14236,Solidcore Systems,c:42668,47000000,40000000,acquired 427 | 740,,,Octavian,c:42672,0,15000000,closed 428 | 741,,,Zilker Labs,c:42842,0,27000000,closed 429 | 743,,,U.S. Fiduciary,c:43133,0,9000000,closed 430 | 745,,,QuikCycle,c:43162,0,17800000,closed 431 | 747,Zillow,c:959,RentJuice,c:43183,40000000,6693782,acquired 432 | 749,Facebook,c:5,Instagram,c:43233,1010470000,57500000,acquired 433 | 752,comScore,c:1926,AdXpose,c:4336,22000000,22800000,acquired 434 | 753,,,MyNines,c:43386,0,500000,closed 435 | 754,,,Stickybits,c:43416,0,1900000,closed 436 | 755,,,Levanta,c:43458,0,8000000,closed 437 | 756,,,Mail.com Media Corporation,c:43480,0,35000000,closed 438 | 759,,,Axial Biotech,c:43724,0,19400000,closed 439 | 760,,,Nascentric,c:43741,0,13400000,closed 440 | 762,TIBCO Software,c:4967,Loyalty Lab,c:43832,23000000,11000000,acquired 441 | 764,,,Kolorific,c:43873,0,7000000,closed 442 | 765,,,Defywire,c:43875,0,10600000,closed 443 | 766,,,Jacent Technologies,c:43877,0,13700000,closed 444 | 767,,,Bioenvision,c:43915,0,7410000,closed 445 | 769,,,Transparency Software,c:43948,0,12050000,closed 446 | 771,Marchex,c:674,Jingle Networks,c:44,62500000,88651133,acquired 447 | 772,,,Oasys Mobile,c:44064,0,8000000,closed 448 | 775,,,Food Quality Sensor International,c:44305,0,5600000,closed 449 | 776,,,Sampa,c:444,0,1310000,closed 450 | 778,Intuit,c:2537,Demandforce,c:44504,423500000,10119801,acquired 451 | 778,Intuit,c:2537,Demandforce,c:44504,423500000,10119801,acquired 452 | 779,Intuit,c:2537,Demandforce,c:44504,423500000,10119801,acquired 453 | 779,Intuit,c:2537,Demandforce,c:44504,423500000,10119801,acquired 454 | 782,,,Yamisee,c:44616,0,700000,closed 455 | 783,Akamai Technologies,c:424,Netli,c:44650,170000000,18000000,acquired 456 | 784,GlobalSCAPE,c:6478,TappIn,c:44693,17000000,2293122,acquired 457 | 786,,,Entertainment Media Works,c:45111,0,4000000,closed 458 | 787,,,Athena Design Systems,c:45112,0,4000000,closed 459 | 788,,,Skyway Software,c:45114,0,4000000,closed 460 | 789,,,U4EA Wireless,c:45115,0,19300000,closed 461 | 792,,,Azaire Networks,c:45339,0,21000000,closed 462 | 793,,,Amitive,c:45396,0,16000000,closed 463 | 794,,,DanceJam,c:454,0,4500000,closed 464 | 795,,,Trooval,c:45413,0,2387481,closed 465 | 797,Cisco,c:28,Composite Software,c:45428,180000000,14650003,acquired 466 | 800,VMware,c:5990,Integrien,c:45696,100000000,24510540,acquired 467 | 802,,,Avolent,c:45709,0,9000000,closed 468 | 803,,,Intrusic,c:45792,0,16000000,closed 469 | 804,,,EventVue,c:458,0,455000,closed 470 | 806,,,NextMedium,c:46042,0,9500000,closed 471 | 807,,,Pyxis Mobile,c:46070,0,24150000,closed 472 | 808,,,Xytis,c:46073,0,24500000,closed 473 | 810,Shire,c:53654,SARcode Bioscience,c:46168,160000000,73000000,acquired 474 | 811,,,Mendocino Software,c:46186,0,19700000,closed 475 | 812,Google,c:59,Aardvark,c:4622,50000000,6000000,acquired 476 | 813,,,CebaTech,c:46259,0,8500000,closed 477 | 816,LinkedIn,c:176,Rapportive,c:46586,15000000,1000000,acquired 478 | 817,ADOR,c:53254,Off & Away,c:46835,5000000,3250000,acquired 479 | 818,,,MyCabbage,c:46863,0,950000,closed 480 | 819,,,FireStar Software,c:46889,0,980000,closed 481 | 820,,,HeadCase Humanufacturing,c:46893,0,900000,closed 482 | 822,PayPal,c:333,Braintree,c:46943,800000000,69000000,acquired 483 | 824,Glam Media,c:149,Ning by Glam Media,c:47,150000000,119000000,acquired 484 | 825,,,Giiv,c:47115,0,3350000,closed 485 | 826,,,Offermatic,c:47270,0,4500000,closed 486 | 827,,,Tipping Bucket,c:47341,0,72000,closed 487 | 828,,,International Battery,c:47440,0,35000000,closed 488 | 829,,,Infineta Systems,c:47587,0,30000000,closed 489 | 831,,,SpinTheCam,c:47662,0,150000,closed 490 | 832,,,Atrica,c:47689,0,22200000,closed 491 | 833,Cisco,c:28,Navini Networks,c:47848,330000000,30920000,acquired 492 | 834,Cox Enterprises,c:4797,Adify,c:4794,300000000,27000000,acquired 493 | 835,,,People to Remember,c:47950,0,25000,closed 494 | 836,,,Center'd,c:4829,0,10400000,closed 495 | 837,,,NeuroTherapeutics Pharma,c:48376,0,55000000,closed 496 | 838,,,Auvitek International,c:48640,0,9000000,closed 497 | 839,Yahoo!,c:29,BlueLithium,c:487,300000000,11500000,acquired 498 | 841,Amazon,c:317,Quidsi,c:49052,545000000,78500000,acquired 499 | 842,,,Loudcaster,c:49236,0,53000,closed 500 | 843,,,Cerona Networks,c:49341,0,2600000,closed 501 | 844,Getty Images,c:629,WireImage,c:49442,202000000,15000000,acquired 502 | 846,,,Helicomm,c:49621,0,1000000,closed 503 | 855,Bio-Rad Laboratories,c:40611,QuantaLife,c:50208,162000000,20757182,acquired 504 | 856,Cadence Pharmaceuticals,c:37734,Incline Therapeutics,c:50209,285000000,64940000,acquired 505 | 857,,,Skyrider,c:5043,0,17000000,closed 506 | 858,,,Opzi,c:50449,0,1015000,closed 507 | 859,Constant Contact,c:3524,SinglePlatform,c:50727,100000000,4575000,acquired 508 | 860,,,Zango,c:509,0,40000000,closed 509 | 861,,,Network Chemistry,c:50927,0,6000000,closed 510 | 862,,,Wirama,c:5095,0,1000000,closed 511 | 864,,,Abound Solar,c:51023,0,510000000,closed 512 | 869,GREE,c:15609,OpenFeint,c:51306,104000000,3000000,acquired 513 | 872,,,DotBlu,c:516,0,2250000,closed 514 | 873,,,Lama Lab,c:51623,0,1213066,closed 515 | 874,,,Wellcore,c:51745,0,2000000,closed 516 | 875,Covidien,c:31915,BARRX Medical,c:51890,325000000,15000000,acquired 517 | 876,Monitise,c:17661,ClairMail,c:5192,173000000,34100000,acquired 518 | 877,,,Hurricane Party,c:51969,0,20000,closed 519 | 878,Sonic Solutions,c:4804,Simple Star,c:522,6000000,7100000,acquired 520 | 881,Under Armour,c:42816,MapMyFitness,c:52406,150000000,23500000,acquired 521 | 882,Airbnb,c:9049,Fondu,c:52566,575000,575000,acquired 522 | 883,,,Dilithium Networks,c:5264,0,98170000,closed 523 | 884,,,Plethora Technology,c:52694,0,1000000,closed 524 | 885,Adobe Systems,c:283,Efficient Frontier,c:5306,400000000,6000000,acquired 525 | 887,,,GeekStatus,c:53304,0,435953,closed 526 | 889,,,TelASIC Communications,c:53471,0,26000000,closed 527 | 891,,,PacketHop,c:53814,0,10000000,closed 528 | 892,,,Inaura,c:53831,0,8500000,closed 529 | 893,Twitter,c:12,AdGrok,c:53885,10000000,470000,acquired 530 | 894,,,T-Networks,c:53946,0,5750000,closed 531 | 895,,,Wesabe,c:54,0,4700000,closed 532 | 896,,,Blue Vector Systems,c:54066,0,4000000,closed 533 | 897,Stratasys,c:148258,MakerBot,c:54077,403000000,10000000,acquired 534 | 898,American Express,c:1640,Sometrics,c:5411,30000000,5550000,acquired 535 | 899,,,Invicta Networks,c:54177,0,200000,closed 536 | 901,,,Smooth-Stone,c:54488,0,48000000,closed 537 | 905,ValueClick,c:840,Greystripe,c:549,75000000,17600000,acquired 538 | 907,,,ezzai - how to arabia,c:55044,0,45000000,closed 539 | 910,VeriFone,c:32951,WAY Systems,c:55354,9000000,22000000,acquired 540 | 914,,,Fashism,c:55436,0,1000000,closed 541 | 915,,,Wander (f. YongoPal),c:55592,0,185000,closed 542 | 916,,,PANTA Systems,c:55829,0,9500000,closed 543 | 917,Teradata,c:25106,Aster Data Systems,c:5585,263000000,53000000,acquired 544 | 918,Opsware,c:4999,iConclude,c:5587,53000000,12500000,acquired 545 | 921,,,RippleFunction,c:56478,0,20000,closed 546 | 922,,,Sionex,c:56568,0,12000000,closed 547 | 923,,,Generic Media,c:56580,0,2500000,closed 548 | 924,GlaxoSmithKline,c:5663,Sirtris Pharmaceuticals,c:5662,720000000,82000000,acquired 549 | 925,,,UniversityLyfe,c:56858,0,500000,closed 550 | 926,Yahoo!,c:29,Qwiki,c:56989,50000000,10500000,acquired 551 | 929,,,SpiralFrog,c:575,0,45000000,closed 552 | 931,,,Visiprise,c:57923,0,32000000,closed 553 | 932,,,Sidecar.me,c:5800,0,5100000,closed 554 | 937,,,Tutorspree,c:58420,0,1800000,closed 555 | 941,,,Klir Technologies,c:58560,0,8700000,closed 556 | 942,,,Dormzy,c:58696,0,250000,closed 557 | 943,,,Hashable,c:58699,0,4000000,closed 558 | 944,Twitter,c:12,MoPub,c:58944,350000000,18500000,acquired 559 | 945,,,MessageGate,c:58998,0,5000000,closed 560 | 946,,,Sychron Advanced Technologies,c:59018,0,5000000,closed 561 | 947,,,Nettle,c:59054,0,500000,closed 562 | 950,,,Diversion,c:59298,0,300000,closed 563 | 952,Opera Software,c:2584,Skyfire Labs,c:5936,155000000,40800000,acquired 564 | 953,,,Aptera,c:5952,0,36300000,closed 565 | 955,,,CafeBots,c:59709,0,5000000,closed 566 | 956,Google,c:59,Jambool,c:5986,70000000,6000000,acquired 567 | 958,O2,c:38385,Jajah,c:60,207000000,33000000,acquired 568 | 961,Cisco,c:28,IronPort Systems,c:6012,830000000,61500000,acquired 569 | 962,,,Blue Buzz Network,c:60186,0,1310000,closed 570 | 963,,,Kiha Software,c:60243,0,20000000,closed 571 | 964,,,World Blender,c:60352,0,12000,closed 572 | 967,,,TeleFlip,c:6049,0,4900000,closed 573 | 968,,,Lazarus Therapeutics,c:60516,0,100000,closed 574 | 973,,,Inertia Beverage Group,c:6150,0,25500100,closed 575 | 974,,,Flud,c:61592,0,2100000,closed 576 | 975,,,Gamook,c:6171,0,1500000,closed 577 | 976,AppDirect,c:74323,Standing Cloud,c:61783,9000000,10000003,acquired 578 | 977,,,DotSpots,c:6219,0,300000,closed 579 | 980,CondÚ Nast,c:318,ZipList,c:62432,14000000,4500000,acquired 580 | 982,Google,c:59,YouTube,c:63,1650000000,11500000,acquired 581 | 984,Symantec,c:3320,PasswordBank,c:63053,0,3500000,acquired 582 | 986,,,Slipstream,c:63217,0,20000,closed 583 | 987,,,Cozi Group,c:6340,0,26390819,closed 584 | 989,,,Lifestander,c:63621,0,31000,closed 585 | 990,Apple,c:1654,Quattro Wireless,c:637,275000000,28300000,acquired 586 | 991,,,Weatherista,c:63731,0,500000,closed 587 | 998,American Express,c:1640,Accertify,c:6520,150000000,4700000,acquired 588 | 999,,,Bevvy,c:65389,0,100000,closed 589 | 1000,,,Help Me Rent Magazine,c:65601,0,500000,closed 590 | 1002,ADOR,c:53254,Plixi,c:65620,10000000,2600000,acquired 591 | 1003,,,Inhale Digital,c:65806,0,1300000,closed 592 | 1007,,,GreenGoose!,c:66706,0,1200000,closed 593 | 1010,,,Pipewise,c:67806,0,3500000,closed 594 | 1016,,,Loud3r,c:6854,0,3529175,closed 595 | 1018,,,Swaptree Inc.,c:6964,0,11300000,closed 596 | 1021,,,Define My Style,c:70021,0,690000,closed 597 | 1025,,,StyleTrek,c:70661,0,1500000,closed 598 | 1026,,,Loosecubes,c:70893,0,9030000,closed 599 | 1027,,,Triples Media,c:71120,0,100000,closed 600 | 1028,,,nprogress,c:71372,0,1700000,closed 601 | 1030,,,FlyCast,c:7163,0,2100000,closed 602 | 1031,SolarCity,c:12352,Zep Solar,c:71639,158000000,10571182,acquired 603 | 1032,,,Color Labs Inc.,c:71852,0,41000000,closed 604 | 1033,Google,c:59,GrandCentral,c:72,60000000,4000000,acquired 605 | 1034,,,rPath,c:7207,0,32500000,closed 606 | 1035,MySpace,c:494,imeem,c:725,1000000,24750000,acquired 607 | 1036,Google,c:59,AdMob,c:726,750000000,46800000,acquired 608 | 1040,SoundCloud Inc.,c:6745,Instinctiv,c:7329,10000000,1420698,acquired 609 | 1041,,,Fortisphere,c:7345,0,10000000,closed 610 | 1042,Facebook,c:5,Onavo,c:73586,150000000,13000000,acquired 611 | 1044,LivingSocial,c:8583,SocialMedia.com,c:741,3000000,10000000,acquired 612 | 1049,Cvent,c:3399,CrowdCompass,c:74928,10000000,1300000,acquired 613 | 1051,,,Solmentum,c:75500,0,325000,closed 614 | 1052,,,Sonya Labs,c:7561,0,25000,closed 615 | 1053,,,Million Dollar Earth,c:75796,0,30000,closed 616 | 1055,Google,c:59,PeakStream,c:7613,20300000,22000000,acquired 617 | 1056,EMC,c:966,Mozy,c:762,76000000,1900000,acquired 618 | 1057,,,Sonar.me,c:76274,0,200000,closed 619 | 1059,,,CatchFree,c:76463,0,5500000,closed 620 | 1061,,,Plizy,c:76543,0,5200000,closed 621 | 1064,Concur Technologies,c:18871,TripIt,c:767,120000000,13100000,acquired 622 | 1065,,,Tripl,c:76765,0,900000,closed 623 | 1066,AOL,c:351,Adap.tv,c:77,405000000,48500000,acquired 624 | 1067,Intuit,c:2537,Mint.com,c:771,170000000,31775000,acquired 625 | 1069,,,Flowgram,c:7748,0,1300000,closed 626 | 1070,Cisco,c:28,JouleX,c:78759,107000000,17000000,acquired 627 | 1072,,,HelpMeRent.com,c:79089,0,154000,closed 628 | 1074,,,Gigi Hill,c:79149,0,4000000,closed 629 | 1075,LinkedIn,c:176,Pulse,c:79431,90000000,9800000,acquired 630 | 1076,,,TrustDegrees,c:79456,0,11000,closed 631 | 1081,,,Eyeona,c:80259,0,2000000,closed 632 | 1082,,,Matatena Games,c:80400,0,120000,closed 633 | 1085,,,RatePoint,c:810,0,24500000,closed 634 | 1087,,,Caterva,c:81474,0,2408500,closed 635 | 1088,,,Moprise,c:81475,0,2238055,closed 636 | 1091,,,skyrockit,c:81886,0,2600000,closed 637 | 1092,,,neoSaej,c:8251,0,13130000,closed 638 | 1096,,,MyDoorman,c:83173,0,100000,closed 639 | 1097,,,ItsGoinOn,c:83176,0,100000,closed 640 | 1099,,,Eons,c:833,0,32000000,closed 641 | 1101,AOL,c:351,5min Media,c:835,65000000,12800000,acquired 642 | 1102,,,Myrio,c:8351,0,20500000,closed 643 | 1103,Twitter,c:12,Crashlytics,c:83692,100000000,6000000,acquired 644 | 1105,,,CrowdSling,c:84008,0,40000,closed 645 | 1107,Facebook,c:5,Parse,c:84320,90000000,7000000,acquired 646 | 1108,,,Link_A_Media Devices,c:84867,0,47218498,closed 647 | 1109,,,Lexy,c:8494,0,1708000,closed 648 | 1111,,,Cuil,c:856,0,33000000,closed 649 | 1113,,,Carbonetworks,c:8596,0,5000000,closed 650 | 1115,,,Netgamix Inc,c:86120,0,132885,closed 651 | 1118,Rimage,c:55913,Qumu,c:8718,52000000,24750000,acquired 652 | 1121,Yahoo!,c:29,Xobni,c:886,48000000,41752000,acquired 653 | 1122,Google,c:59,Admeld,c:8972,400000000,30000000,acquired 654 | 1123,VMware,c:5990,SpringSource,c:8990,362000000,25000000,acquired 655 | 1126,GoDaddy,c:3169,Locu,c:90087,70000000,4600000,acquired 656 | 1127,,,Six Degrees Games,c:9038,0,17999998,closed 657 | 1128,,,BrightQube,c:904,0,850000,closed 658 | 1129,eBay,c:20,Hunch,c:9050,80000000,19200000,acquired 659 | 1130,DeNA,c:9636,ngmoco,c:9068,400000000,44800000,acquired 660 | 1131,,,Local Marketers,c:9085,0,4100000,closed 661 | 1132,,,LeisureLogix,c:9106,0,5900000,closed 662 | 1133,,,Mix & Meet,c:915,0,400000,closed 663 | 1136,,,Sim Ops Studios,c:933,0,698866,closed 664 | 1138,Compuware,c:6651,dynaTrace software,c:9373,256000000,21900000,acquired 665 | 1139,IBM,c:3246,Worklight,c:938,70000000,17600000,acquired 666 | 1143,AuthenTec,c:26999,Atrua Technologies,c:9665,600000000,13000000,acquired 667 | 1144,,,Sequoia Communications,c:9668,0,74000000,closed 668 | 1146,Salesforce,c:1972,Salesforce Buddy Media,c:967,689000000,90000000,acquired 669 | 1147,Visa,c:17066,PlaySpan,c:971,190000000,46300000,acquired 670 | 1148,,,OfferLounge,c:9730,0,120000,closed 671 | 1154,Yahoo!,c:29,Zimbra,c:997,350000000,14500000,acquired 672 | -------------------------------------------------------------------------------- /d3/data/us-states.json: -------------------------------------------------------------------------------- 1 | {"type":"FeatureCollection","features":[ 2 | 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