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February 17, 2017 13:53
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solution to exponea challenge
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 102, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Overwriting utils.py\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%%writefile utils.py\n", | |
| "\n", | |
| "BOOL_COLS = {\n", | |
| " 'Dependent-Company Status': str, # to bool\n", | |
| " 'Has the team size grown': str, # to bool\n", | |
| " 'Presence of a top angel or venture fund in previous round of investment': str, # to bool\n", | |
| " 'Worked in top companies': str, # to bool\n", | |
| " 'Have been part of startups in the past?': str, # to bool\n", | |
| " 'Have been part of successful startups in the past?': str, # to bool\n", | |
| " 'Was he or she partner in Big 5 consulting?': str, # to bool\n", | |
| " 'Consulting experience?': str, # to bool\n", | |
| " 'Focus on consumer data?': str, # to bool\n", | |
| " 'Subscription based business': str, # to bool\n", | |
| " 'Capital intensive business e.g. e-commerce, Engineering products and operations can also \\\n", | |
| "cause a business to be capital intensive': str, # to bool\n", | |
| " 'Crowdsourcing based business': str, # to bool\n", | |
| " 'Crowdfunding based business': str, # to bool\n", | |
| " 'Machine Learning based business': str, # to bool\n", | |
| " 'Predictive Analytics business': str, # to bool\n", | |
| " 'Speech analytics business': str, # to bool\n", | |
| " 'Prescriptive analytics business': str, # to bool\n", | |
| " 'Big Data Business': str, # to bool\n", | |
| " 'Cross-Channel Analytics/ marketing channels': str, # to bool\n", | |
| " 'Owns data or not? (monetization of data) e.g. Factual': str, # to bool\n", | |
| " 'Is the company an aggregator/market place? e.g. Bluekai': str, # to bool\n", | |
| " 'Exposure across the globe': str, # to bool\n", | |
| " 'Relevance of education to venture': str, # to bool\n", | |
| " 'Relevance of experience to venture': str, # to bool\n", | |
| " 'Pricing Strategy': str, # to bool\n", | |
| " 'Hyper localisation': str, # to bool\n", | |
| " 'Long term relationship with other founders': str, # to bool\n", | |
| " 'Proprietary or patent position (competitive position)': str, # to bool\n", | |
| " 'Barriers of entry for the competitors': str, # to bool\n", | |
| " 'Company awards': str, # to bool\n", | |
| " 'Controversial history of founder or co founder': str, # to bool\n", | |
| " 'Legal risk and intellectual property': str, # to bool\n", | |
| " 'Technical proficiencies to analyse and interpret unstructured data': str, # to bool\n", | |
| " 'Solutions offered': str, # to bool\n", | |
| " 'Invested through global incubation competitions?': str, # to bool\n", | |
| "}\n", | |
| "\n", | |
| "DATETIME_COLS = {\n", | |
| " 'Est. Founding Date': str, # to datetime\n", | |
| " 'Last Funding Date': str, # to datetime and number of days\n", | |
| "}\n", | |
| "\n", | |
| "CATEGORY_COLS = {\n", | |
| " 'year of founding': int,\n", | |
| " 'Industry of company': str, # to category\n", | |
| " 'Country of company': str, # to category\n", | |
| " 'Continent of company': str, # to category\n", | |
| " 'Number of Sales Support material': str, # to category\n", | |
| " 'Average size of companies worked for in the past': str, # to category\n", | |
| " 'Product or service company?': str, # to category\n", | |
| " 'Focus on structured or unstructured data': str, # to category\n", | |
| " 'Catering to product/service across verticals': str, # to category\n", | |
| " 'Focus on private or public data?': str, # to category\n", | |
| " 'Cloud or platform based serive/product?': str, # to category\n", | |
| " 'Local or global player': str, # to category\n", | |
| " 'Linear or Non-linear business model': str, # to category\n", | |
| " 'Number of of Partners of company': str, # to category\n", | |
| " 'Online or offline venture - physical location based business or online venture?': str, # to category\n", | |
| " 'B2C or B2B venture?': str, # to category\n", | |
| " \"Top forums like 'Tech crunch' or 'Venture beat' \\\n", | |
| "talking about the company/model - How much is it being talked about?\": str, # to category\n", | |
| " 'Average Years of experience for founder and co founder': str, # to category\n", | |
| " 'Breadth of experience across verticals': str, # to category\n", | |
| " 'Highest education': str, # to category\n", | |
| " 'Specialization of highest education': str, # to category\n", | |
| " 'Degree from a Tier 1 or Tier 2 university?': str, # to category\n", | |
| " 'Renowned in professional circle': str, # to category\n", | |
| " 'Experience in selling and building products': str, # to category\n", | |
| " 'Top management similarity': str, # to category\n", | |
| " 'Number of of Research publications': str, # to category\n", | |
| " 'Team Composition score': str, # to category\n", | |
| " 'Dificulty of Obtaining Work force': str, # to category\n", | |
| " 'Time to market service or product': str, # to category\n", | |
| " 'Employee benefits and salary structures': str, # to category \n", | |
| " 'Client Reputation': str, # to category\n", | |
| " 'Disruptiveness of technology': str, # to category\n", | |
| " 'Survival through recession, based on existence of the \\\n", | |
| "company through recession times': str, # to category\n", | |
| " 'Gartner hype cycle stage': str, # to category\n", | |
| " 'Time to maturity of technology (in years)': str, # to category\n", | |
| "}\n", | |
| "\n", | |
| "CATEGORY_COLS = dict.fromkeys(CATEGORY_COLS.keys(), 'category')\n", | |
| "\n", | |
| "INDEX_COL = {\n", | |
| " 'Company_Name': str, # to index\n", | |
| "}\n", | |
| "\n", | |
| "NUMERIC_COLS = {\n", | |
| " 'Age of company in years': int,\n", | |
| " 'Internet Activity Score': float,\n", | |
| "# 'Short Description of company profile': str,\n", | |
| "# 'Focus functions of company': str,\n", | |
| "# 'Investors': str,\n", | |
| " 'Employee Count': int,\n", | |
| " 'Employees count MoM change': float,\n", | |
| " 'Last Funding Amount': float,\n", | |
| " 'Number of Investors in Seed': int, \n", | |
| " 'Number of Investors in Angel and or VC': int,\n", | |
| " 'Number of Co-founders': int,\n", | |
| " 'Number of of advisors': int,\n", | |
| " 'Team size Senior leadership': int,\n", | |
| " 'Team size all employees': int,\n", | |
| " 'Number of of repeat investors': int,\n", | |
| " 'Experience in Fortune 100 organizations': int,\n", | |
| " 'Experience in Fortune 500 organizations': int,\n", | |
| " 'Experience in Fortune 1000 organizations': int,\n", | |
| " 'Years of education': int,\n", | |
| " 'Number of Recognitions for Founders and Co-founders': int,\n", | |
| " 'Skills score': float,\n", | |
| " 'google page rank of company website': int,\n", | |
| " 'Industry trend in investing': float,\n", | |
| " 'Number of Direct competitors': int,\n", | |
| " 'Employees per year of company existence': float,\n", | |
| " 'Last round of funding received (in milionUSD)': float,\n", | |
| " 'Time to 1st investment (in months)': int,\n", | |
| " 'Avg time to investment - average across all rounds, measured from previous investment': float,\n", | |
| " 'Percent_skill_Entrepreneurship': float,\n", | |
| " 'Percent_skill_Operations': float,\n", | |
| " 'Percent_skill_Engineering': float,\n", | |
| " 'Percent_skill_Marketing': float,\n", | |
| " 'Percent_skill_Leadership': float,\n", | |
| " 'Percent_skill_Data Science': float,\n", | |
| " 'Percent_skill_Business Strategy': float,\n", | |
| " 'Percent_skill_Product Management': float,\n", | |
| " 'Percent_skill_Sales': float,\n", | |
| " 'Percent_skill_Domain': float,\n", | |
| " 'Percent_skill_Law': float,\n", | |
| " 'Percent_skill_Consulting': float,\n", | |
| " 'Percent_skill_Finance': float,\n", | |
| " 'Percent_skill_Investment': float,\n", | |
| " 'Renown score': int\n", | |
| "}\n", | |
| "\n", | |
| "ALL_COLS = dict(INDEX_COL.items() + \n", | |
| " NUMERIC_COLS.items() + \n", | |
| " DATETIME_COLS.items() + \n", | |
| " BOOL_COLS.items() + \n", | |
| " CATEGORY_COLS.items())" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Data \n", | |
| "## Loading and munging" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 303, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import numpy as np\n", | |
| "import pandas as pd\n", | |
| "\n", | |
| "import utils\n", | |
| "reload(utils)\n", | |
| "from utils import *\n", | |
| "\n", | |
| "data_df = pd.read_csv('/home/adrian/Data/Kaggle/ExponeaChallenge/Startup_Data.csv', dtype=str)\n", | |
| "data_df = data_df[ALL_COLS.keys()]\n", | |
| "data_df.set_index(INDEX_COL.keys(), inplace=True)\n", | |
| "\n", | |
| "data_df[NUMERIC_COLS.keys()] = \\\n", | |
| " data_df[NUMERIC_COLS.keys()].applymap(\n", | |
| " lambda x: np.nan if isinstance(x, str) and len(x.split(' ')) > 1 else np.float16(x))\n", | |
| "\n", | |
| "data_df[BOOL_COLS.keys()+CATEGORY_COLS.keys()] = \\\n", | |
| " data_df[BOOL_COLS.keys()+CATEGORY_COLS.keys()].apply(lambda x: x.astype('category'), axis=1)\n", | |
| "\n", | |
| "data_df[DATETIME_COLS.keys()] = \\\n", | |
| " data_df[DATETIME_COLS.keys()].apply(lambda x: pd.to_datetime(x, format='%d/%M/%Y'), axis=1)\n", | |
| "\n", | |
| "dump_cols = ['Specialization of highest education', 'Industry of company']\n", | |
| "data_df.drop(labels=dump_cols+DATETIME_COLS.keys(), axis=1, inplace=True)\n", | |
| "\n", | |
| "target_col = ['Dependent-Company Status']\n", | |
| "dummy_df = pd.get_dummies(data_df[[c for c in BOOL_COLS.keys()+CATEGORY_COLS.keys()\n", | |
| " if c not in dump_cols+target_col]])\n", | |
| "new_data_df = pd.concat([data_df[target_col+NUMERIC_COLS.keys()], dummy_df], axis=1)\n", | |
| "new_data_df.shape\n", | |
| "\n", | |
| "print new_data_df[target_col[0]].unique()\n", | |
| "\n", | |
| "new_data_df[target_col[0]] = new_data_df[target_col[0]].map({'Success': 1, 'Failed': 0})" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 158, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Dependent-Company Status</th>\n", | |
| " <th>Time to 1st investment (in months)</th>\n", | |
| " <th>Years of education</th>\n", | |
| " <th>Percent_skill_Product Management</th>\n", | |
| " <th>Number of of repeat investors</th>\n", | |
| " <th>Industry trend in investing</th>\n", | |
| " <th>Renown score</th>\n", | |
| " <th>Number of Co-founders</th>\n", | |
| " <th>Percent_skill_Consulting</th>\n", | |
| " <th>Percent_skill_Sales</th>\n", | |
| " <th>...</th>\n", | |
| " <th>Linear or Non-linear business model_Non-Linear</th>\n", | |
| " <th>Number of of Partners of company_Few</th>\n", | |
| " <th>Number of of Partners of company_Many</th>\n", | |
| " <th>Number of of Partners of company_No Info</th>\n", | |
| " <th>Number of of Partners of company_None</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_High</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_Low</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_Medium</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_No Info</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_None</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Company_Name</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
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| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
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| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>Company1</th>\n", | |
| " <td>1</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>21.0</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>4.0</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>...</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Company2</th>\n", | |
| " <td>1</td>\n", | |
| " <td>10.0</td>\n", | |
| " <td>21.0</td>\n", | |
| " <td>10.882812</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>2.941406</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
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| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Company3</th>\n", | |
| " <td>1</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>18.0</td>\n", | |
| " <td>9.398438</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
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| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Company4</th>\n", | |
| " <td>1</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>18.0</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>4.0</td>\n", | |
| " <td>5.0</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.000000</td>\n", | |
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| " <td>0</td>\n", | |
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| " <td>0</td>\n", | |
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| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Company5</th>\n", | |
| " <td>1</td>\n", | |
| " <td>13.0</td>\n", | |
| " <td>18.0</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.000000</td>\n", | |
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| " <td>1</td>\n", | |
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| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>5 rows × 370 columns</p>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Dependent-Company Status Time to 1st investment (in months) \\\n", | |
| "Company_Name \n", | |
| "Company1 1 NaN \n", | |
| "Company2 1 10.0 \n", | |
| "Company3 1 2.0 \n", | |
| "Company4 1 1.0 \n", | |
| "Company5 1 13.0 \n", | |
| "\n", | |
| " Years of education Percent_skill_Product Management \\\n", | |
| "Company_Name \n", | |
| "Company1 21.0 0.000000 \n", | |
| "Company2 21.0 10.882812 \n", | |
| "Company3 18.0 9.398438 \n", | |
| "Company4 18.0 0.000000 \n", | |
| "Company5 18.0 0.000000 \n", | |
| "\n", | |
| " Number of of repeat investors Industry trend in investing \\\n", | |
| "Company_Name \n", | |
| "Company1 4.0 2.0 \n", | |
| "Company2 0.0 3.0 \n", | |
| "Company3 0.0 3.0 \n", | |
| "Company4 0.0 4.0 \n", | |
| "Company5 0.0 3.0 \n", | |
| "\n", | |
| " Renown score Number of Co-founders Percent_skill_Consulting \\\n", | |
| "Company_Name \n", | |
| "Company1 0.0 1.0 0.0 \n", | |
| "Company2 8.0 2.0 0.0 \n", | |
| "Company3 9.0 3.0 0.0 \n", | |
| "Company4 5.0 2.0 0.0 \n", | |
| "Company5 6.0 1.0 0.0 \n", | |
| "\n", | |
| " Percent_skill_Sales \\\n", | |
| "Company_Name \n", | |
| "Company1 0.000000 \n", | |
| "Company2 2.941406 \n", | |
| "Company3 0.000000 \n", | |
| "Company4 0.000000 \n", | |
| "Company5 0.000000 \n", | |
| "\n", | |
| " ... \\\n", | |
| "Company_Name ... \n", | |
| "Company1 ... \n", | |
| "Company2 ... \n", | |
| "Company3 ... \n", | |
| "Company4 ... \n", | |
| "Company5 ... \n", | |
| "\n", | |
| " Linear or Non-linear business model_Non-Linear \\\n", | |
| "Company_Name \n", | |
| "Company1 0 \n", | |
| "Company2 1 \n", | |
| "Company3 1 \n", | |
| "Company4 1 \n", | |
| "Company5 1 \n", | |
| "\n", | |
| " Number of of Partners of company_Few \\\n", | |
| "Company_Name \n", | |
| "Company1 0 \n", | |
| "Company2 1 \n", | |
| "Company3 1 \n", | |
| "Company4 1 \n", | |
| "Company5 1 \n", | |
| "\n", | |
| " Number of of Partners of company_Many \\\n", | |
| "Company_Name \n", | |
| "Company1 0 \n", | |
| "Company2 0 \n", | |
| "Company3 0 \n", | |
| "Company4 0 \n", | |
| "Company5 0 \n", | |
| "\n", | |
| " Number of of Partners of company_No Info \\\n", | |
| "Company_Name \n", | |
| "Company1 0 \n", | |
| "Company2 0 \n", | |
| "Company3 0 \n", | |
| "Company4 0 \n", | |
| "Company5 0 \n", | |
| "\n", | |
| " Number of of Partners of company_None \\\n", | |
| "Company_Name \n", | |
| "Company1 1 \n", | |
| "Company2 0 \n", | |
| "Company3 0 \n", | |
| "Company4 0 \n", | |
| "Company5 0 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_High \\\n", | |
| "Company_Name \n", | |
| "Company1 1 \n", | |
| "Company2 0 \n", | |
| "Company3 0 \n", | |
| "Company4 0 \n", | |
| "Company5 0 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_Low \\\n", | |
| "Company_Name \n", | |
| "Company1 0 \n", | |
| "Company2 1 \n", | |
| "Company3 1 \n", | |
| "Company4 0 \n", | |
| "Company5 1 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_Medium \\\n", | |
| "Company_Name \n", | |
| "Company1 0 \n", | |
| "Company2 0 \n", | |
| "Company3 0 \n", | |
| "Company4 1 \n", | |
| "Company5 0 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_No Info \\\n", | |
| "Company_Name \n", | |
| "Company1 0 \n", | |
| "Company2 0 \n", | |
| "Company3 0 \n", | |
| "Company4 0 \n", | |
| "Company5 0 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_None \n", | |
| "Company_Name \n", | |
| "Company1 0 \n", | |
| "Company2 0 \n", | |
| "Company3 0 \n", | |
| "Company4 0 \n", | |
| "Company5 0 \n", | |
| "\n", | |
| "[5 rows x 370 columns]" | |
| ] | |
| }, | |
| "execution_count": 158, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "new_data_df.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Observing missing values" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 292, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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CAAAAAAfmxhtvzE9+8pM+75k+fXqmT59epUR9a6gSnP7nLxYAAAAAgB396Z/+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\n77357ne/mxUrVuz2+qBBgzJ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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f9903833410>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "import missingno as msno\n", | |
| "msno.matrix(new_data_df)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Train & test set creation" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 307, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# handling missing values / NaN or invalid ones, like inf\n", | |
| "new_data_df.fillna(0, inplace=True) # starting simple\n", | |
| "inf_cols = new_data_df.columns[new_data_df.applymap(np.isinf).any(axis=0).nonzero()]\n", | |
| "new_data_df.drop(labels=inf_cols, axis=1, inplace=True)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 308, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "len_data = len(new_data_df)\n", | |
| "random_idx = np.random.choice(len_data, len_data, replace=False)\n", | |
| "limit_idx = np.arange(len_data) < int(len_data * .75)\n", | |
| "train_idx = random_idx[limit_idx]\n", | |
| "test_idx = random_idx[~limit_idx]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 309, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "X_train, y_train = new_data_df.ix[train_idx, 1:], new_data_df.ix[train_idx, 0]\n", | |
| "X_test, y_test = new_data_df.ix[test_idx, 1:], new_data_df.ix[test_idx, 0]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 225, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Time to 1st investment (in months)</th>\n", | |
| " <th>Years of education</th>\n", | |
| " <th>Percent_skill_Product Management</th>\n", | |
| " <th>Number of of repeat investors</th>\n", | |
| " <th>Industry trend in investing</th>\n", | |
| " <th>Renown score</th>\n", | |
| " <th>Number of Co-founders</th>\n", | |
| " <th>Percent_skill_Consulting</th>\n", | |
| " <th>Percent_skill_Sales</th>\n", | |
| " <th>Percent_skill_Domain</th>\n", | |
| " <th>...</th>\n", | |
| " <th>Linear or Non-linear business model_Non-Linear</th>\n", | |
| " <th>Number of of Partners of company_Few</th>\n", | |
| " <th>Number of of Partners of company_Many</th>\n", | |
| " <th>Number of of Partners of company_No Info</th>\n", | |
| " <th>Number of of Partners of company_None</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_High</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_Low</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_Medium</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_No Info</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_None</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Company_Name</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>Company201</th>\n", | |
| " <td>5.0</td>\n", | |
| " <td>18.0</td>\n", | |
| " <td>0.00</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>4.0</td>\n", | |
| " <td>4.0</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.000</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Company239</th>\n", | |
| " <td>16.0</td>\n", | |
| " <td>21.0</td>\n", | |
| " <td>0.00</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>5.0</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.000</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Company138</th>\n", | |
| " <td>12.0</td>\n", | |
| " <td>21.0</td>\n", | |
| " <td>6.25</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>9.375</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Company453</th>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.00</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.000</td>\n", | |
| " <td>...</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Company223</th>\n", | |
| " <td>15.0</td>\n", | |
| " <td>18.0</td>\n", | |
| " <td>0.00</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.000</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>5 rows × 367 columns</p>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Time to 1st investment (in months) Years of education \\\n", | |
| "Company_Name \n", | |
| "Company201 5.0 18.0 \n", | |
| "Company239 16.0 21.0 \n", | |
| "Company138 12.0 21.0 \n", | |
| "Company453 0.0 0.0 \n", | |
| "Company223 15.0 18.0 \n", | |
| "\n", | |
| " Percent_skill_Product Management Number of of repeat investors \\\n", | |
| "Company_Name \n", | |
| "Company201 0.00 6.0 \n", | |
| "Company239 0.00 0.0 \n", | |
| "Company138 6.25 0.0 \n", | |
| "Company453 0.00 0.0 \n", | |
| "Company223 0.00 0.0 \n", | |
| "\n", | |
| " Industry trend in investing Renown score \\\n", | |
| "Company_Name \n", | |
| "Company201 4.0 4.0 \n", | |
| "Company239 2.0 5.0 \n", | |
| "Company138 3.0 1.0 \n", | |
| "Company453 0.0 0.0 \n", | |
| "Company223 2.0 1.0 \n", | |
| "\n", | |
| " Number of Co-founders Percent_skill_Consulting \\\n", | |
| "Company_Name \n", | |
| "Company201 2.0 0.0 \n", | |
| "Company239 2.0 0.0 \n", | |
| "Company138 1.0 0.0 \n", | |
| "Company453 0.0 0.0 \n", | |
| "Company223 0.0 0.0 \n", | |
| "\n", | |
| " Percent_skill_Sales Percent_skill_Domain \\\n", | |
| "Company_Name \n", | |
| "Company201 0.0 0.000 \n", | |
| "Company239 0.0 0.000 \n", | |
| "Company138 0.0 9.375 \n", | |
| "Company453 0.0 0.000 \n", | |
| "Company223 0.0 0.000 \n", | |
| "\n", | |
| " ... \\\n", | |
| "Company_Name ... \n", | |
| "Company201 ... \n", | |
| "Company239 ... \n", | |
| "Company138 ... \n", | |
| "Company453 ... \n", | |
| "Company223 ... \n", | |
| "\n", | |
| " Linear or Non-linear business model_Non-Linear \\\n", | |
| "Company_Name \n", | |
| "Company201 1 \n", | |
| "Company239 1 \n", | |
| "Company138 1 \n", | |
| "Company453 0 \n", | |
| "Company223 1 \n", | |
| "\n", | |
| " Number of of Partners of company_Few \\\n", | |
| "Company_Name \n", | |
| "Company201 1 \n", | |
| "Company239 0 \n", | |
| "Company138 1 \n", | |
| "Company453 0 \n", | |
| "Company223 0 \n", | |
| "\n", | |
| " Number of of Partners of company_Many \\\n", | |
| "Company_Name \n", | |
| "Company201 0 \n", | |
| "Company239 0 \n", | |
| "Company138 0 \n", | |
| "Company453 0 \n", | |
| "Company223 0 \n", | |
| "\n", | |
| " Number of of Partners of company_No Info \\\n", | |
| "Company_Name \n", | |
| "Company201 0 \n", | |
| "Company239 0 \n", | |
| "Company138 0 \n", | |
| "Company453 1 \n", | |
| "Company223 0 \n", | |
| "\n", | |
| " Number of of Partners of company_None \\\n", | |
| "Company_Name \n", | |
| "Company201 0 \n", | |
| "Company239 1 \n", | |
| "Company138 0 \n", | |
| "Company453 0 \n", | |
| "Company223 1 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_High \\\n", | |
| "Company_Name \n", | |
| "Company201 0 \n", | |
| "Company239 0 \n", | |
| "Company138 1 \n", | |
| "Company453 0 \n", | |
| "Company223 0 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_Low \\\n", | |
| "Company_Name \n", | |
| "Company201 0 \n", | |
| "Company239 1 \n", | |
| "Company138 0 \n", | |
| "Company453 0 \n", | |
| "Company223 1 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_Medium \\\n", | |
| "Company_Name \n", | |
| "Company201 1 \n", | |
| "Company239 0 \n", | |
| "Company138 0 \n", | |
| "Company453 0 \n", | |
| "Company223 0 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_No Info \\\n", | |
| "Company_Name \n", | |
| "Company201 0 \n", | |
| "Company239 0 \n", | |
| "Company138 0 \n", | |
| "Company453 1 \n", | |
| "Company223 0 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_None \n", | |
| "Company_Name \n", | |
| "Company201 0 \n", | |
| "Company239 0 \n", | |
| "Company138 0 \n", | |
| "Company453 0 \n", | |
| "Company223 0 \n", | |
| "\n", | |
| "[5 rows x 367 columns]" | |
| ] | |
| }, | |
| "execution_count": 225, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "X_train.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 226, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "Company_Name\n", | |
| "Company201 1\n", | |
| "Company239 1\n", | |
| "Company138 0\n", | |
| "Company453 0\n", | |
| "Company223 1\n", | |
| "Name: Dependent-Company Status, dtype: int64" | |
| ] | |
| }, | |
| "execution_count": 226, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "y_train.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Quick training data exploration" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 296, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import matplotlib.pyplot as plt\n", | |
| "%matplotlib inline\n", | |
| "import seaborn as sb\n", | |
| "sb.set()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 247, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x7f98e6e8c5d0>" | |
| ] | |
| }, | |
| "execution_count": 247, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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HHOgc/7777tvv8XH66afnW9/61hHdXweyZ79f7YMf/OBrfq1/uPvo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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f98e6e79950>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "X_train[[c for c in NUMERIC_COLS.keys() if c not in inf_cols]].plot(kind='box', figsize=[20, 10])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 248, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Time to 1st investment (in months)</th>\n", | |
| " <th>Years of education</th>\n", | |
| " <th>Percent_skill_Product Management</th>\n", | |
| " <th>Number of of repeat investors</th>\n", | |
| " <th>Industry trend in investing</th>\n", | |
| " <th>Renown score</th>\n", | |
| " <th>Number of Co-founders</th>\n", | |
| " <th>Percent_skill_Consulting</th>\n", | |
| " <th>Percent_skill_Sales</th>\n", | |
| " <th>Percent_skill_Domain</th>\n", | |
| " <th>...</th>\n", | |
| " <th>Linear or Non-linear business model_Non-Linear</th>\n", | |
| " <th>Number of of Partners of company_Few</th>\n", | |
| " <th>Number of of Partners of company_Many</th>\n", | |
| " <th>Number of of Partners of company_No Info</th>\n", | |
| " <th>Number of of Partners of company_None</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_High</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_Low</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_Medium</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_No Info</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_None</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>count</th>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>...</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>mean</th>\n", | |
| " <td>11.550847</td>\n", | |
| " <td>15.878531</td>\n", | |
| " <td>2.860145</td>\n", | |
| " <td>0.564972</td>\n", | |
| " <td>2.316384</td>\n", | |
| " <td>2.903955</td>\n", | |
| " <td>1.884181</td>\n", | |
| " <td>0.449169</td>\n", | |
| " <td>2.968618</td>\n", | |
| " <td>4.217476</td>\n", | |
| " <td>...</td>\n", | |
| " <td>0.672316</td>\n", | |
| " <td>0.141243</td>\n", | |
| " <td>0.025424</td>\n", | |
| " <td>0.220339</td>\n", | |
| " <td>0.612994</td>\n", | |
| " <td>0.064972</td>\n", | |
| " <td>0.511299</td>\n", | |
| " <td>0.166667</td>\n", | |
| " <td>0.177966</td>\n", | |
| " <td>0.079096</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>std</th>\n", | |
| " <td>16.647962</td>\n", | |
| " <td>8.390906</td>\n", | |
| " <td>4.554673</td>\n", | |
| " <td>1.203166</td>\n", | |
| " <td>1.359642</td>\n", | |
| " <td>2.890217</td>\n", | |
| " <td>1.166399</td>\n", | |
| " <td>2.157175</td>\n", | |
| " <td>5.314433</td>\n", | |
| " <td>7.711089</td>\n", | |
| " <td>...</td>\n", | |
| " <td>0.470033</td>\n", | |
| " <td>0.348765</td>\n", | |
| " <td>0.157631</td>\n", | |
| " <td>0.415062</td>\n", | |
| " <td>0.487754</td>\n", | |
| " <td>0.246825</td>\n", | |
| " <td>0.500580</td>\n", | |
| " <td>0.373205</td>\n", | |
| " <td>0.383026</td>\n", | |
| " <td>0.270271</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>min</th>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>...</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>25%</th>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>18.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>2.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>...</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>50%</th>\n", | |
| " <td>6.000000</td>\n", | |
| " <td>18.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>2.000000</td>\n", | |
| " <td>2.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>75%</th>\n", | |
| " <td>15.000000</td>\n", | |
| " <td>21.000000</td>\n", | |
| " <td>5.554688</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>5.554688</td>\n", | |
| " <td>5.554688</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>max</th>\n", | |
| " <td>120.000000</td>\n", | |
| " <td>25.000000</td>\n", | |
| " <td>25.000000</td>\n", | |
| " <td>10.000000</td>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>11.000000</td>\n", | |
| " <td>7.000000</td>\n", | |
| " <td>20.000000</td>\n", | |
| " <td>33.343750</td>\n", | |
| " <td>44.437500</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>8 rows × 367 columns</p>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Time to 1st investment (in months) Years of education \\\n", | |
| "count 354.000000 354.000000 \n", | |
| "mean 11.550847 15.878531 \n", | |
| "std 16.647962 8.390906 \n", | |
| "min 0.000000 0.000000 \n", | |
| "25% 1.000000 18.000000 \n", | |
| "50% 6.000000 18.000000 \n", | |
| "75% 15.000000 21.000000 \n", | |
| "max 120.000000 25.000000 \n", | |
| "\n", | |
| " Percent_skill_Product Management Number of of repeat investors \\\n", | |
| "count 354.000000 354.000000 \n", | |
| "mean 2.860145 0.564972 \n", | |
| "std 4.554673 1.203166 \n", | |
| "min 0.000000 0.000000 \n", | |
| "25% 0.000000 0.000000 \n", | |
| "50% 0.000000 0.000000 \n", | |
| "75% 5.554688 1.000000 \n", | |
| "max 25.000000 10.000000 \n", | |
| "\n", | |
| " Industry trend in investing Renown score Number of Co-founders \\\n", | |
| "count 354.000000 354.000000 354.000000 \n", | |
| "mean 2.316384 2.903955 1.884181 \n", | |
| "std 1.359642 2.890217 1.166399 \n", | |
| "min 0.000000 0.000000 0.000000 \n", | |
| "25% 2.000000 0.000000 1.000000 \n", | |
| "50% 3.000000 2.000000 2.000000 \n", | |
| "75% 3.000000 5.000000 3.000000 \n", | |
| "max 5.000000 11.000000 7.000000 \n", | |
| "\n", | |
| " Percent_skill_Consulting Percent_skill_Sales Percent_skill_Domain \\\n", | |
| "count 354.000000 354.000000 354.000000 \n", | |
| "mean 0.449169 2.968618 4.217476 \n", | |
| "std 2.157175 5.314433 7.711089 \n", | |
| "min 0.000000 0.000000 0.000000 \n", | |
| "25% 0.000000 0.000000 0.000000 \n", | |
| "50% 0.000000 0.000000 0.000000 \n", | |
| "75% 0.000000 5.554688 5.554688 \n", | |
| "max 20.000000 33.343750 44.437500 \n", | |
| "\n", | |
| " ... \\\n", | |
| "count ... \n", | |
| "mean ... \n", | |
| "std ... \n", | |
| "min ... \n", | |
| "25% ... \n", | |
| "50% ... \n", | |
| "75% ... \n", | |
| "max ... \n", | |
| "\n", | |
| " Linear or Non-linear business model_Non-Linear \\\n", | |
| "count 354.000000 \n", | |
| "mean 0.672316 \n", | |
| "std 0.470033 \n", | |
| "min 0.000000 \n", | |
| "25% 0.000000 \n", | |
| "50% 1.000000 \n", | |
| "75% 1.000000 \n", | |
| "max 1.000000 \n", | |
| "\n", | |
| " Number of of Partners of company_Few \\\n", | |
| "count 354.000000 \n", | |
| "mean 0.141243 \n", | |
| "std 0.348765 \n", | |
| "min 0.000000 \n", | |
| "25% 0.000000 \n", | |
| "50% 0.000000 \n", | |
| "75% 0.000000 \n", | |
| "max 1.000000 \n", | |
| "\n", | |
| " Number of of Partners of company_Many \\\n", | |
| "count 354.000000 \n", | |
| "mean 0.025424 \n", | |
| "std 0.157631 \n", | |
| "min 0.000000 \n", | |
| "25% 0.000000 \n", | |
| "50% 0.000000 \n", | |
| "75% 0.000000 \n", | |
| "max 1.000000 \n", | |
| "\n", | |
| " Number of of Partners of company_No Info \\\n", | |
| "count 354.000000 \n", | |
| "mean 0.220339 \n", | |
| "std 0.415062 \n", | |
| "min 0.000000 \n", | |
| "25% 0.000000 \n", | |
| "50% 0.000000 \n", | |
| "75% 0.000000 \n", | |
| "max 1.000000 \n", | |
| "\n", | |
| " Number of of Partners of company_None \\\n", | |
| "count 354.000000 \n", | |
| "mean 0.612994 \n", | |
| "std 0.487754 \n", | |
| "min 0.000000 \n", | |
| "25% 0.000000 \n", | |
| "50% 1.000000 \n", | |
| "75% 1.000000 \n", | |
| "max 1.000000 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_High \\\n", | |
| "count 354.000000 \n", | |
| "mean 0.064972 \n", | |
| "std 0.246825 \n", | |
| "min 0.000000 \n", | |
| "25% 0.000000 \n", | |
| "50% 0.000000 \n", | |
| "75% 0.000000 \n", | |
| "max 1.000000 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_Low \\\n", | |
| "count 354.000000 \n", | |
| "mean 0.511299 \n", | |
| "std 0.500580 \n", | |
| "min 0.000000 \n", | |
| "25% 0.000000 \n", | |
| "50% 1.000000 \n", | |
| "75% 1.000000 \n", | |
| "max 1.000000 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_Medium \\\n", | |
| "count 354.000000 \n", | |
| "mean 0.166667 \n", | |
| "std 0.373205 \n", | |
| "min 0.000000 \n", | |
| "25% 0.000000 \n", | |
| "50% 0.000000 \n", | |
| "75% 0.000000 \n", | |
| "max 1.000000 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_No Info \\\n", | |
| "count 354.000000 \n", | |
| "mean 0.177966 \n", | |
| "std 0.383026 \n", | |
| "min 0.000000 \n", | |
| "25% 0.000000 \n", | |
| "50% 0.000000 \n", | |
| "75% 0.000000 \n", | |
| "max 1.000000 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_None \n", | |
| "count 354.000000 \n", | |
| "mean 0.079096 \n", | |
| "std 0.270271 \n", | |
| "min 0.000000 \n", | |
| "25% 0.000000 \n", | |
| "50% 0.000000 \n", | |
| "75% 0.000000 \n", | |
| "max 1.000000 \n", | |
| "\n", | |
| "[8 rows x 367 columns]" | |
| ] | |
| }, | |
| "execution_count": 248, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "X_train.describe()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Modeling\n", | |
| "## Regularized logistic regression with cross-validation" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 345, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from sklearn.linear_model import LogisticRegressionCV\n", | |
| "from sklearn.preprocessing import Normalizer, StandardScaler\n", | |
| "from sklearn.pipeline import Pipeline\n", | |
| "import sklearn\n", | |
| "from sklearn.metrics import accuracy_score, roc_auc_score, roc_curve, auc\n", | |
| "import pandas as pd" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 419, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "l1_logit_cv = LogisticRegressionCV(solver='liblinear', n_jobs=-1, cv=5, penalty='l1')\n", | |
| "l2_logit_cv = LogisticRegressionCV(solver='liblinear', n_jobs=-1, cv=5, penalty='l2')\n", | |
| "normalizer = Normalizer()\n", | |
| "scaler = StandardScaler()\n", | |
| "base_pipe = [('scaler', scaler), ('normalizer', normalizer)]\n", | |
| "l1_logit_cv_pipe = Pipeline(base_pipe+[('l1_logit', sklearn.clone(l1_logit_cv))])\n", | |
| "l2_logit_cv_pipe = Pipeline(base_pipe+[('l2_logit', sklearn.clone(l2_logit_cv))])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## l1\n", | |
| "### non-scaled inputs" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 420, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# fit\n", | |
| "l1_logit_cv = l1_logit_cv.fit(X_train.values, y_train.values) # scaling\n", | |
| "# accuracy\n", | |
| "l1_cv_train_acc = l1_logit_cv.score(X_train.values, y_train.values)\n", | |
| "l1_cv_test_acc = l1_logit_cv.score(X_test.values, y_test.values)\n", | |
| "# roc auc\n", | |
| "l1_cv_train_roc_auc = roc_auc_score(y_true=y_train.values, y_score=l1_logit_cv.predict(X_train.values))\n", | |
| "l1_cv_test_roc_auc = roc_auc_score(y_true=y_test.values, y_score=l1_logit_cv.predict(X_test.values))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### scaled inputs" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 421, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# fit\n", | |
| "l1_logit_cv_pipe = l1_logit_cv_pipe.fit(X_train.values, y_train.values) # scaling\n", | |
| "# accuracy\n", | |
| "l1_cv_pipe_train_acc = l1_logit_cv_pipe.score(X_train.values, y_train.values)\n", | |
| "l1_cv_pipe_test_acc = l1_logit_cv_pipe.score(X_test.values, y_test.values)\n", | |
| "# roc auc\n", | |
| "l1_cv_pipe_train_roc_auc = roc_auc_score(y_true=y_train.values, y_score=l1_logit_cv_pipe.predict(X_train.values))\n", | |
| "l1_cv_pipe_test_roc_auc = roc_auc_score(y_true=y_test.values, y_score=l1_logit_cv_pipe.predict(X_test.values))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## l2\n", | |
| "### non-scaled inputs" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 422, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# fit\n", | |
| "l2_logit_cv = l2_logit_cv.fit(X_train.values, y_train.values) # not scaled\n", | |
| "# accuracy\n", | |
| "l2_cv_train_acc = l2_logit_cv.score(X_train.values, y_train.values)\n", | |
| "l2_cv_test_acc = l2_logit_cv.score(X_test.values, y_test.values)\n", | |
| "# roc auc\n", | |
| "l2_cv_train_roc_auc = roc_auc_score(y_true=y_train.values, y_score=l2_logit_cv.predict(X_train.values))\n", | |
| "l2_cv_test_roc_auc = roc_auc_score(y_true=y_test.values, y_score=l2_logit_cv.predict(X_test.values))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### scaled inputs" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 423, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# fit\n", | |
| "l2_logit_cv_pipe = l2_logit_cv_pipe.fit(X_train.values, y_train.values) # scaling\n", | |
| "# accuracy\n", | |
| "l2_cv_pipe_train_acc = l2_logit_cv_pipe.score(X_train.values, y_train.values)\n", | |
| "l2_cv_pipe_test_acc = l2_logit_cv_pipe.score(X_test.values, y_test.values)\n", | |
| "# roc auc\n", | |
| "l2_cv_pipe_train_roc_auc = roc_auc_score(y_true=y_train.values, y_score=l2_logit_cv_pipe.predict(X_train.values))\n", | |
| "l2_cv_pipe_test_roc_auc = roc_auc_score(y_true=y_test.values, y_score=l2_logit_cv_pipe.predict(X_test.values))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Best fit" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 436, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "acc = [l1_cv_train_acc, l1_cv_test_acc, l2_cv_train_acc, l2_cv_test_acc,\n", | |
| " l1_cv_pipe_train_acc, l1_cv_pipe_test_acc, l2_cv_pipe_train_acc, l2_cv_pipe_test_acc]\n", | |
| "roc_auc = [l1_cv_train_roc_auc, l1_cv_test_roc_auc, l2_cv_train_roc_auc, l2_cv_test_roc_auc,\n", | |
| " l1_cv_pipe_train_roc_auc, l1_cv_pipe_test_roc_auc, l2_cv_pipe_train_roc_auc, l2_cv_pipe_test_roc_auc]\n", | |
| "\n", | |
| "sets = ['train', 'test']*4\n", | |
| "logits = ['l1_logit']*2 + ['l2_logit']*2 + ['l1_logit_pipe']*2 + ['l2_logit_pipe']*2\n", | |
| "\n", | |
| "results_df = pd.DataFrame(dict(accuracy=acc, roc_auc=roc_auc, logits=logits, sets=sets))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 440, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def add_numbers(ax):\n", | |
| " for p in ax.patches:\n", | |
| " height = p.get_height()\n", | |
| " ax.text(p.get_x()+p.get_width()/2.,\n", | |
| " height,\n", | |
| " '{:1.2f}'.format(height),\n", | |
| " ha=\"center\")\n", | |
| " \n", | |
| "def barplot(y, df, ax):\n", | |
| " sb.barplot(x='logits', y=y, hue='sets', data=df, ax=ax)\n", | |
| " ax.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,\n", | |
| " ncol=2, mode=\"expand\", borderaxespad=0.)\n", | |
| " add_numbers(ax)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 442, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f98e65d4bd0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "sb.set(font_scale=1.75)\n", | |
| "fig, axs = plt.subplots(1, 2, figsize=[20, 7])\n", | |
| "barplot('accuracy', results_df, axs[0])\n", | |
| "barplot('roc_auc', results_df, axs[1])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Best fit -> l1_logit_cv_pipe\n", | |
| "### ROC plot for best fit" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 429, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "logit_y_score = l1_logit_cv_pipe.predict(X_test.values)\n", | |
| "logit_fpr, logit_tpr, _ = roc_curve(y_test, logit_y_score)\n", | |
| "logit_roc_auc = auc(fpr, tpr)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 430, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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+fRxdpoiISJ6mMCkiIo+kcePGYTabWb16NZ9++inR0dH4+/vTr18/XnzxRdts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\nIYQQQohs+x8m5YdPbIYrewAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f98d32cb650>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=[15, 7])\n", | |
| "lw = 2\n", | |
| "plt.plot(fpr, tpr, color='darkorange', lw=lw,\n", | |
| " label='ROC curve (area = {:.02f})'.format(logit_roc_auc))\n", | |
| "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n", | |
| "plt.xlim([0.0, 1.0])\n", | |
| "plt.ylim([0.0, 1.05])\n", | |
| "plt.xlabel('False Positive Rate')\n", | |
| "plt.ylabel('True Positive Rate')\n", | |
| "plt.title('Receiver operating characteristic example')\n", | |
| "plt.legend(loc=\"lower right\")\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Gradient boosted classification trees with cross-validation\n", | |
| "(because I am lazy to grid-search, using SMAC[1] to find hyper-parameters) \n", | |
| "[1] http://www.cs.ubc.ca/labs/beta/Projects/SMAC/, need to pip-install python wrapper from https://github.com/sfalkner/pySMAC" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 317, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import xgboost\n", | |
| "import pysmac\n", | |
| "from sklearn.model_selection import cross_val_score\n", | |
| "from functools import partial\n", | |
| "\n", | |
| "def _cv_score(estimator, X, y, scoring, cv, n_jobs, verbose, pre_dispatch, **params):\n", | |
| " \"\"\"\n", | |
| " Pickle-safe function for setting estimator parameters and runninc\n", | |
| " cross-validation used for hyperparameter optimization.\n", | |
| " \"\"\"\n", | |
| " estimator.set_params(**params)\n", | |
| " return - cross_val_score(estimator=estimator, X=X, y=y, scoring=scoring,\n", | |
| " cv=cv, n_jobs=n_jobs, verbose=verbose,\n", | |
| " pre_dispatch=pre_dispatch).mean()\n", | |
| "param_dists = {\n", | |
| " 'n_estimators': ('integer', [1, 1000], 100),\n", | |
| " \n", | |
| " 'max_delta_step': ('real', [0.1, 1.], 0.5),\n", | |
| " 'max_depth': ('integer', [1, 10], 5),\n", | |
| " 'min_child_weight': ('integer', [1, 10], 5),\n", | |
| " \n", | |
| " 'gamma': ('real', [0.01, 10.], 0.5),\n", | |
| " \n", | |
| " 'subsample': ('real', [.1, .9], 0.5),\n", | |
| " 'colsample_bylevel' :('real', [0.1, 0.9], 0.5),\n", | |
| " 'colsample_bytree' :('real', [0.1, 0.9], 0.5),\n", | |
| " \n", | |
| " 'reg_alpha': ('real', [0., 100.], 0.),\n", | |
| " 'reg_lambda': ('real', [0., 100.], 0.),\n", | |
| " \n", | |
| " 'learning_rate': ('real', [1e-5, 1.], 1e-1),\n", | |
| "}\n", | |
| "\n", | |
| "# hyper-parameter optimization uzing SMAC optimizer\n", | |
| "opt = pysmac.SMAC_optimizer()\n", | |
| "opt.smac_options['java_executable'] = 'java'# '/usr/bin/java -d32'\n", | |
| "\n", | |
| "strat_cv = sklearn.model_selection.StratifiedKFold(n_splits=5)\n", | |
| "\n", | |
| "xgb_clf = xgboost.XGBClassifier(silent=True, nthread=-1)\n", | |
| "\n", | |
| "cv_objective = partial(_cv_score, estimator=xgb_clf, X=X_train.values, y=y_train.values,\n", | |
| " scoring='neg_log_loss', cv=strat_cv, n_jobs=1,\n", | |
| " verbose=1, pre_dispatch='2*n_jobs')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 318, | |
| "metadata": { | |
| "collapsed": false, | |
| "scrolled": true | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.4s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.6s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.5s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.5s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.4s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.7s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.4s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.3s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.1s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 2.2s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.6s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 2.2s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.4s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.7s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.1s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.7s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.9s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.3s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.5s finished\n", | |
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| ] | |
| } | |
| ], | |
| "source": [ | |
| "value, parameters = \\\n", | |
| " opt.minimize(cv_objective, # the function to be minimized\n", | |
| " 100, # the maximum number of evaluations\n", | |
| " param_dists,\n", | |
| " num_runs=1,\n", | |
| " num_procs=5)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 327, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "{'colsample_bylevel': 0.3732398142090083,\n", | |
| " 'colsample_bytree': 0.42691583166718006,\n", | |
| " 'gamma': 0.15175438536162458,\n", | |
| " 'learning_rate': 0.15060483902929975,\n", | |
| " 'max_delta_step': 0.5524967888544415,\n", | |
| " 'max_depth': 1,\n", | |
| " 'min_child_weight': 1,\n", | |
| " 'n_estimators': 554,\n", | |
| " 'reg_alpha': 1.8733695708081588,\n", | |
| " 'reg_lambda': 18.02124059035505,\n", | |
| " 'subsample': 0.8450620447893316}" | |
| ] | |
| }, | |
| "execution_count": 327, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "parameters" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 325, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "xgb_clf.set_params(**parameters)\n", | |
| "xgb_clf = xgb_clf.fit(X_train.values, y_train.values)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 326, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Train accuracy 0.95197740113\n", | |
| "Test accuracy 0.915254237288\n", | |
| "Train ROC AUC 0.946741854637\n", | |
| "Train ROC AUC 0.889452011403\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print 'Train accuracy', xgb_clf.score(X_train.values, y_train.values)\n", | |
| "print 'Test accuracy', xgb_clf.score(X_test.values, y_test.values)\n", | |
| "\n", | |
| "print 'Train ROC AUC', roc_auc_score(y_true=y_train.values, y_score=xgb_clf.predict(X_train.values))\n", | |
| "print 'Train ROC AUC', roc_auc_score(y_true=y_test.values, y_score=xgb_clf.predict(X_test.values))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 353, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "xgb_y_score = xgb_clf.predict(X_test.values)\n", | |
| "xgb_fpr, xgb_tpr, _ = roc_curve(y_test.values, xgb_y_score)\n", | |
| "xgb_roc_auc = auc(xgb_fpr, xgb_tpr)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 412, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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84e4yRURE8jSFSRERuS+99dZbmM1mVqxYwQcffEBiYiJFihShd+/ePPvss/bZ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\naNMmwuSEIiIi8ktXLZNvvvkmhmFcdt8//vEP4GKxNAwDi8WiMlmB+X3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fNDuOSIW2YMFO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\nXCdjD6rsjgmRF3l57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FIIIYQQQgghRK5JMimEEEIIIYQQItckmRRCCCGEEEIIkWuSTAoh\nhBBCCCGEyDVJJoUQQgghhBBC5Jokk0IIIYQQQgghck2SSSGEEEIIIYQQufZ/fPR4dE1BiL4AAAAA\nSUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f98d2f11810>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=[15, 7])\n", | |
| "lw = 2\n", | |
| "plt.plot(xgb_fpr, xgb_tpr, color='darkorange',\n", | |
| " lw=lw, label='ROC curve (area = {:.02f})'.format(xgb_roc_auc))\n", | |
| "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n", | |
| "plt.xlim([0.0, 1.0])\n", | |
| "plt.ylim([0.0, 1.05])\n", | |
| "plt.xlabel('False Positive Rate')\n", | |
| "plt.ylabel('True Positive Rate')\n", | |
| "plt.title('Receiver operating characteristic example')\n", | |
| "plt.legend(loc=\"lower right\")\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Conclusion: xgb_clf better generalization than l1_logit_cv_pipe" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Further work might inlcude: \n", | |
| " * modify and include dumped cols\n", | |
| " * try smarter missing values handling (instead of 0, use either previous/next value or interpolated values or KNN imputatoin)\n", | |
| " * try feature engineering by exploring data distributions in more detailed way\n", | |
| " * dimensionality reduction using PCA/KPCA or T-SNE\n", | |
| " * different model, e.g. SVC" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 2", | |
| "language": "python", | |
| "name": "python2" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 2 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython2", | |
| "version": "2.7.6" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 1 | |
| } |
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 102, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Overwriting utils.py\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%%writefile utils.py\n", | |
| "\n", | |
| "BOOL_COLS = {\n", | |
| " 'Dependent-Company Status': str, # to bool\n", | |
| " 'Has the team size grown': str, # to bool\n", | |
| " 'Presence of a top angel or venture fund in previous round of investment': str, # to bool\n", | |
| " 'Worked in top companies': str, # to bool\n", | |
| " 'Have been part of startups in the past?': str, # to bool\n", | |
| " 'Have been part of successful startups in the past?': str, # to bool\n", | |
| " 'Was he or she partner in Big 5 consulting?': str, # to bool\n", | |
| " 'Consulting experience?': str, # to bool\n", | |
| " 'Focus on consumer data?': str, # to bool\n", | |
| " 'Subscription based business': str, # to bool\n", | |
| " 'Capital intensive business e.g. e-commerce, Engineering products and operations can also \\\n", | |
| "cause a business to be capital intensive': str, # to bool\n", | |
| " 'Crowdsourcing based business': str, # to bool\n", | |
| " 'Crowdfunding based business': str, # to bool\n", | |
| " 'Machine Learning based business': str, # to bool\n", | |
| " 'Predictive Analytics business': str, # to bool\n", | |
| " 'Speech analytics business': str, # to bool\n", | |
| " 'Prescriptive analytics business': str, # to bool\n", | |
| " 'Big Data Business': str, # to bool\n", | |
| " 'Cross-Channel Analytics/ marketing channels': str, # to bool\n", | |
| " 'Owns data or not? (monetization of data) e.g. Factual': str, # to bool\n", | |
| " 'Is the company an aggregator/market place? e.g. Bluekai': str, # to bool\n", | |
| " 'Exposure across the globe': str, # to bool\n", | |
| " 'Relevance of education to venture': str, # to bool\n", | |
| " 'Relevance of experience to venture': str, # to bool\n", | |
| " 'Pricing Strategy': str, # to bool\n", | |
| " 'Hyper localisation': str, # to bool\n", | |
| " 'Long term relationship with other founders': str, # to bool\n", | |
| " 'Proprietary or patent position (competitive position)': str, # to bool\n", | |
| " 'Barriers of entry for the competitors': str, # to bool\n", | |
| " 'Company awards': str, # to bool\n", | |
| " 'Controversial history of founder or co founder': str, # to bool\n", | |
| " 'Legal risk and intellectual property': str, # to bool\n", | |
| " 'Technical proficiencies to analyse and interpret unstructured data': str, # to bool\n", | |
| " 'Solutions offered': str, # to bool\n", | |
| " 'Invested through global incubation competitions?': str, # to bool\n", | |
| "}\n", | |
| "\n", | |
| "DATETIME_COLS = {\n", | |
| " 'Est. Founding Date': str, # to datetime\n", | |
| " 'Last Funding Date': str, # to datetime and number of days\n", | |
| "}\n", | |
| "\n", | |
| "CATEGORY_COLS = {\n", | |
| " 'year of founding': int,\n", | |
| " 'Industry of company': str, # to category\n", | |
| " 'Country of company': str, # to category\n", | |
| " 'Continent of company': str, # to category\n", | |
| " 'Number of Sales Support material': str, # to category\n", | |
| " 'Average size of companies worked for in the past': str, # to category\n", | |
| " 'Product or service company?': str, # to category\n", | |
| " 'Focus on structured or unstructured data': str, # to category\n", | |
| " 'Catering to product/service across verticals': str, # to category\n", | |
| " 'Focus on private or public data?': str, # to category\n", | |
| " 'Cloud or platform based serive/product?': str, # to category\n", | |
| " 'Local or global player': str, # to category\n", | |
| " 'Linear or Non-linear business model': str, # to category\n", | |
| " 'Number of of Partners of company': str, # to category\n", | |
| " 'Online or offline venture - physical location based business or online venture?': str, # to category\n", | |
| " 'B2C or B2B venture?': str, # to category\n", | |
| " \"Top forums like 'Tech crunch' or 'Venture beat' \\\n", | |
| "talking about the company/model - How much is it being talked about?\": str, # to category\n", | |
| " 'Average Years of experience for founder and co founder': str, # to category\n", | |
| " 'Breadth of experience across verticals': str, # to category\n", | |
| " 'Highest education': str, # to category\n", | |
| " 'Specialization of highest education': str, # to category\n", | |
| " 'Degree from a Tier 1 or Tier 2 university?': str, # to category\n", | |
| " 'Renowned in professional circle': str, # to category\n", | |
| " 'Experience in selling and building products': str, # to category\n", | |
| " 'Top management similarity': str, # to category\n", | |
| " 'Number of of Research publications': str, # to category\n", | |
| " 'Team Composition score': str, # to category\n", | |
| " 'Dificulty of Obtaining Work force': str, # to category\n", | |
| " 'Time to market service or product': str, # to category\n", | |
| " 'Employee benefits and salary structures': str, # to category \n", | |
| " 'Client Reputation': str, # to category\n", | |
| " 'Disruptiveness of technology': str, # to category\n", | |
| " 'Survival through recession, based on existence of the \\\n", | |
| "company through recession times': str, # to category\n", | |
| " 'Gartner hype cycle stage': str, # to category\n", | |
| " 'Time to maturity of technology (in years)': str, # to category\n", | |
| "}\n", | |
| "\n", | |
| "CATEGORY_COLS = dict.fromkeys(CATEGORY_COLS.keys(), 'category')\n", | |
| "\n", | |
| "INDEX_COL = {\n", | |
| " 'Company_Name': str, # to index\n", | |
| "}\n", | |
| "\n", | |
| "NUMERIC_COLS = {\n", | |
| " 'Age of company in years': int,\n", | |
| " 'Internet Activity Score': float,\n", | |
| "# 'Short Description of company profile': str,\n", | |
| "# 'Focus functions of company': str,\n", | |
| "# 'Investors': str,\n", | |
| " 'Employee Count': int,\n", | |
| " 'Employees count MoM change': float,\n", | |
| " 'Last Funding Amount': float,\n", | |
| " 'Number of Investors in Seed': int, \n", | |
| " 'Number of Investors in Angel and or VC': int,\n", | |
| " 'Number of Co-founders': int,\n", | |
| " 'Number of of advisors': int,\n", | |
| " 'Team size Senior leadership': int,\n", | |
| " 'Team size all employees': int,\n", | |
| " 'Number of of repeat investors': int,\n", | |
| " 'Experience in Fortune 100 organizations': int,\n", | |
| " 'Experience in Fortune 500 organizations': int,\n", | |
| " 'Experience in Fortune 1000 organizations': int,\n", | |
| " 'Years of education': int,\n", | |
| " 'Number of Recognitions for Founders and Co-founders': int,\n", | |
| " 'Skills score': float,\n", | |
| " 'google page rank of company website': int,\n", | |
| " 'Industry trend in investing': float,\n", | |
| " 'Number of Direct competitors': int,\n", | |
| " 'Employees per year of company existence': float,\n", | |
| " 'Last round of funding received (in milionUSD)': float,\n", | |
| " 'Time to 1st investment (in months)': int,\n", | |
| " 'Avg time to investment - average across all rounds, measured from previous investment': float,\n", | |
| " 'Percent_skill_Entrepreneurship': float,\n", | |
| " 'Percent_skill_Operations': float,\n", | |
| " 'Percent_skill_Engineering': float,\n", | |
| " 'Percent_skill_Marketing': float,\n", | |
| " 'Percent_skill_Leadership': float,\n", | |
| " 'Percent_skill_Data Science': float,\n", | |
| " 'Percent_skill_Business Strategy': float,\n", | |
| " 'Percent_skill_Product Management': float,\n", | |
| " 'Percent_skill_Sales': float,\n", | |
| " 'Percent_skill_Domain': float,\n", | |
| " 'Percent_skill_Law': float,\n", | |
| " 'Percent_skill_Consulting': float,\n", | |
| " 'Percent_skill_Finance': float,\n", | |
| " 'Percent_skill_Investment': float,\n", | |
| " 'Renown score': int\n", | |
| "}\n", | |
| "\n", | |
| "ALL_COLS = dict(INDEX_COL.items() + \n", | |
| " NUMERIC_COLS.items() + \n", | |
| " DATETIME_COLS.items() + \n", | |
| " BOOL_COLS.items() + \n", | |
| " CATEGORY_COLS.items())" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Data \n", | |
| "## Loading and munging" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 303, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import numpy as np\n", | |
| "import pandas as pd\n", | |
| "\n", | |
| "import utils\n", | |
| "reload(utils)\n", | |
| "from utils import *\n", | |
| "\n", | |
| "data_df = pd.read_csv('/home/adrian/Data/Kaggle/ExponeaChallenge/Startup_Data.csv', dtype=str)\n", | |
| "data_df = data_df[ALL_COLS.keys()]\n", | |
| "data_df.set_index(INDEX_COL.keys(), inplace=True)\n", | |
| "\n", | |
| "data_df[NUMERIC_COLS.keys()] = \\\n", | |
| " data_df[NUMERIC_COLS.keys()].applymap(\n", | |
| " lambda x: np.nan if isinstance(x, str) and len(x.split(' ')) > 1 else np.float16(x))\n", | |
| "\n", | |
| "data_df[BOOL_COLS.keys()+CATEGORY_COLS.keys()] = \\\n", | |
| " data_df[BOOL_COLS.keys()+CATEGORY_COLS.keys()].apply(lambda x: x.astype('category'), axis=1)\n", | |
| "\n", | |
| "data_df[DATETIME_COLS.keys()] = \\\n", | |
| " data_df[DATETIME_COLS.keys()].apply(lambda x: pd.to_datetime(x, format='%d/%M/%Y'), axis=1)\n", | |
| "\n", | |
| "dump_cols = ['Specialization of highest education', 'Industry of company']\n", | |
| "data_df.drop(labels=dump_cols+DATETIME_COLS.keys(), axis=1, inplace=True)\n", | |
| "\n", | |
| "target_col = ['Dependent-Company Status']\n", | |
| "dummy_df = pd.get_dummies(data_df[[c for c in BOOL_COLS.keys()+CATEGORY_COLS.keys()\n", | |
| " if c not in dump_cols+target_col]])\n", | |
| "new_data_df = pd.concat([data_df[target_col+NUMERIC_COLS.keys()], dummy_df], axis=1)\n", | |
| "new_data_df.shape\n", | |
| "\n", | |
| "print new_data_df[target_col[0]].unique()\n", | |
| "\n", | |
| "new_data_df[target_col[0]] = new_data_df[target_col[0]].map({'Success': 1, 'Failed': 0})" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 158, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Dependent-Company Status</th>\n", | |
| " <th>Time to 1st investment (in months)</th>\n", | |
| " <th>Years of education</th>\n", | |
| " <th>Percent_skill_Product Management</th>\n", | |
| " <th>Number of of repeat investors</th>\n", | |
| " <th>Industry trend in investing</th>\n", | |
| " <th>Renown score</th>\n", | |
| " <th>Number of Co-founders</th>\n", | |
| " <th>Percent_skill_Consulting</th>\n", | |
| " <th>Percent_skill_Sales</th>\n", | |
| " <th>...</th>\n", | |
| " <th>Linear or Non-linear business model_Non-Linear</th>\n", | |
| " <th>Number of of Partners of company_Few</th>\n", | |
| " <th>Number of of Partners of company_Many</th>\n", | |
| " <th>Number of of Partners of company_No Info</th>\n", | |
| " <th>Number of of Partners of company_None</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_High</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_Low</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_Medium</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_No Info</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_None</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Company_Name</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>Company1</th>\n", | |
| " <td>1</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>21.0</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>4.0</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>...</td>\n", | |
| " <td>0</td>\n", | |
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| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Company2</th>\n", | |
| " <td>1</td>\n", | |
| " <td>10.0</td>\n", | |
| " <td>21.0</td>\n", | |
| " <td>10.882812</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>8.0</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>2.941406</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Company3</th>\n", | |
| " <td>1</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>18.0</td>\n", | |
| " <td>9.398438</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>9.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.000000</td>\n", | |
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| " <td>1</td>\n", | |
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| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Company4</th>\n", | |
| " <td>1</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>18.0</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>4.0</td>\n", | |
| " <td>5.0</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>0.0</td>\n", | |
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| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Company5</th>\n", | |
| " <td>1</td>\n", | |
| " <td>13.0</td>\n", | |
| " <td>18.0</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.000000</td>\n", | |
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| " <td>1</td>\n", | |
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| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>5 rows × 370 columns</p>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Dependent-Company Status Time to 1st investment (in months) \\\n", | |
| "Company_Name \n", | |
| "Company1 1 NaN \n", | |
| "Company2 1 10.0 \n", | |
| "Company3 1 2.0 \n", | |
| "Company4 1 1.0 \n", | |
| "Company5 1 13.0 \n", | |
| "\n", | |
| " Years of education Percent_skill_Product Management \\\n", | |
| "Company_Name \n", | |
| "Company1 21.0 0.000000 \n", | |
| "Company2 21.0 10.882812 \n", | |
| "Company3 18.0 9.398438 \n", | |
| "Company4 18.0 0.000000 \n", | |
| "Company5 18.0 0.000000 \n", | |
| "\n", | |
| " Number of of repeat investors Industry trend in investing \\\n", | |
| "Company_Name \n", | |
| "Company1 4.0 2.0 \n", | |
| "Company2 0.0 3.0 \n", | |
| "Company3 0.0 3.0 \n", | |
| "Company4 0.0 4.0 \n", | |
| "Company5 0.0 3.0 \n", | |
| "\n", | |
| " Renown score Number of Co-founders Percent_skill_Consulting \\\n", | |
| "Company_Name \n", | |
| "Company1 0.0 1.0 0.0 \n", | |
| "Company2 8.0 2.0 0.0 \n", | |
| "Company3 9.0 3.0 0.0 \n", | |
| "Company4 5.0 2.0 0.0 \n", | |
| "Company5 6.0 1.0 0.0 \n", | |
| "\n", | |
| " Percent_skill_Sales \\\n", | |
| "Company_Name \n", | |
| "Company1 0.000000 \n", | |
| "Company2 2.941406 \n", | |
| "Company3 0.000000 \n", | |
| "Company4 0.000000 \n", | |
| "Company5 0.000000 \n", | |
| "\n", | |
| " ... \\\n", | |
| "Company_Name ... \n", | |
| "Company1 ... \n", | |
| "Company2 ... \n", | |
| "Company3 ... \n", | |
| "Company4 ... \n", | |
| "Company5 ... \n", | |
| "\n", | |
| " Linear or Non-linear business model_Non-Linear \\\n", | |
| "Company_Name \n", | |
| "Company1 0 \n", | |
| "Company2 1 \n", | |
| "Company3 1 \n", | |
| "Company4 1 \n", | |
| "Company5 1 \n", | |
| "\n", | |
| " Number of of Partners of company_Few \\\n", | |
| "Company_Name \n", | |
| "Company1 0 \n", | |
| "Company2 1 \n", | |
| "Company3 1 \n", | |
| "Company4 1 \n", | |
| "Company5 1 \n", | |
| "\n", | |
| " Number of of Partners of company_Many \\\n", | |
| "Company_Name \n", | |
| "Company1 0 \n", | |
| "Company2 0 \n", | |
| "Company3 0 \n", | |
| "Company4 0 \n", | |
| "Company5 0 \n", | |
| "\n", | |
| " Number of of Partners of company_No Info \\\n", | |
| "Company_Name \n", | |
| "Company1 0 \n", | |
| "Company2 0 \n", | |
| "Company3 0 \n", | |
| "Company4 0 \n", | |
| "Company5 0 \n", | |
| "\n", | |
| " Number of of Partners of company_None \\\n", | |
| "Company_Name \n", | |
| "Company1 1 \n", | |
| "Company2 0 \n", | |
| "Company3 0 \n", | |
| "Company4 0 \n", | |
| "Company5 0 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_High \\\n", | |
| "Company_Name \n", | |
| "Company1 1 \n", | |
| "Company2 0 \n", | |
| "Company3 0 \n", | |
| "Company4 0 \n", | |
| "Company5 0 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_Low \\\n", | |
| "Company_Name \n", | |
| "Company1 0 \n", | |
| "Company2 1 \n", | |
| "Company3 1 \n", | |
| "Company4 0 \n", | |
| "Company5 1 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_Medium \\\n", | |
| "Company_Name \n", | |
| "Company1 0 \n", | |
| "Company2 0 \n", | |
| "Company3 0 \n", | |
| "Company4 1 \n", | |
| "Company5 0 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_No Info \\\n", | |
| "Company_Name \n", | |
| "Company1 0 \n", | |
| "Company2 0 \n", | |
| "Company3 0 \n", | |
| "Company4 0 \n", | |
| "Company5 0 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_None \n", | |
| "Company_Name \n", | |
| "Company1 0 \n", | |
| "Company2 0 \n", | |
| "Company3 0 \n", | |
| "Company4 0 \n", | |
| "Company5 0 \n", | |
| "\n", | |
| "[5 rows x 370 columns]" | |
| ] | |
| }, | |
| "execution_count": 158, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "new_data_df.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Observing missing values" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 292, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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CAAAAAAfmxhtvzE9+8pM+75k+fXqmT59epUR9a6gSnP7nLxYAAAAAgB396Z/+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\n77357ne/mxUrVuz2+qBBgzJ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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f9903833410>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "import missingno as msno\n", | |
| "msno.matrix(new_data_df)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Train & test set creation" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 307, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# handling missing values / NaN or invalid ones, like inf\n", | |
| "new_data_df.fillna(0, inplace=True) # starting simple\n", | |
| "inf_cols = new_data_df.columns[new_data_df.applymap(np.isinf).any(axis=0).nonzero()]\n", | |
| "new_data_df.drop(labels=inf_cols, axis=1, inplace=True)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 308, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "len_data = len(new_data_df)\n", | |
| "random_idx = np.random.choice(len_data, len_data, replace=False)\n", | |
| "limit_idx = np.arange(len_data) < int(len_data * .75)\n", | |
| "train_idx = random_idx[limit_idx]\n", | |
| "test_idx = random_idx[~limit_idx]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 309, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "X_train, y_train = new_data_df.ix[train_idx, 1:], new_data_df.ix[train_idx, 0]\n", | |
| "X_test, y_test = new_data_df.ix[test_idx, 1:], new_data_df.ix[test_idx, 0]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 225, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Time to 1st investment (in months)</th>\n", | |
| " <th>Years of education</th>\n", | |
| " <th>Percent_skill_Product Management</th>\n", | |
| " <th>Number of of repeat investors</th>\n", | |
| " <th>Industry trend in investing</th>\n", | |
| " <th>Renown score</th>\n", | |
| " <th>Number of Co-founders</th>\n", | |
| " <th>Percent_skill_Consulting</th>\n", | |
| " <th>Percent_skill_Sales</th>\n", | |
| " <th>Percent_skill_Domain</th>\n", | |
| " <th>...</th>\n", | |
| " <th>Linear or Non-linear business model_Non-Linear</th>\n", | |
| " <th>Number of of Partners of company_Few</th>\n", | |
| " <th>Number of of Partners of company_Many</th>\n", | |
| " <th>Number of of Partners of company_No Info</th>\n", | |
| " <th>Number of of Partners of company_None</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_High</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_Low</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_Medium</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_No Info</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_None</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Company_Name</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>Company201</th>\n", | |
| " <td>5.0</td>\n", | |
| " <td>18.0</td>\n", | |
| " <td>0.00</td>\n", | |
| " <td>6.0</td>\n", | |
| " <td>4.0</td>\n", | |
| " <td>4.0</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.000</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Company239</th>\n", | |
| " <td>16.0</td>\n", | |
| " <td>21.0</td>\n", | |
| " <td>0.00</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>5.0</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.000</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Company138</th>\n", | |
| " <td>12.0</td>\n", | |
| " <td>21.0</td>\n", | |
| " <td>6.25</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>3.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>9.375</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Company453</th>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.00</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.000</td>\n", | |
| " <td>...</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>Company223</th>\n", | |
| " <td>15.0</td>\n", | |
| " <td>18.0</td>\n", | |
| " <td>0.00</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>2.0</td>\n", | |
| " <td>1.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.0</td>\n", | |
| " <td>0.000</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>1</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " <td>0</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>5 rows × 367 columns</p>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Time to 1st investment (in months) Years of education \\\n", | |
| "Company_Name \n", | |
| "Company201 5.0 18.0 \n", | |
| "Company239 16.0 21.0 \n", | |
| "Company138 12.0 21.0 \n", | |
| "Company453 0.0 0.0 \n", | |
| "Company223 15.0 18.0 \n", | |
| "\n", | |
| " Percent_skill_Product Management Number of of repeat investors \\\n", | |
| "Company_Name \n", | |
| "Company201 0.00 6.0 \n", | |
| "Company239 0.00 0.0 \n", | |
| "Company138 6.25 0.0 \n", | |
| "Company453 0.00 0.0 \n", | |
| "Company223 0.00 0.0 \n", | |
| "\n", | |
| " Industry trend in investing Renown score \\\n", | |
| "Company_Name \n", | |
| "Company201 4.0 4.0 \n", | |
| "Company239 2.0 5.0 \n", | |
| "Company138 3.0 1.0 \n", | |
| "Company453 0.0 0.0 \n", | |
| "Company223 2.0 1.0 \n", | |
| "\n", | |
| " Number of Co-founders Percent_skill_Consulting \\\n", | |
| "Company_Name \n", | |
| "Company201 2.0 0.0 \n", | |
| "Company239 2.0 0.0 \n", | |
| "Company138 1.0 0.0 \n", | |
| "Company453 0.0 0.0 \n", | |
| "Company223 0.0 0.0 \n", | |
| "\n", | |
| " Percent_skill_Sales Percent_skill_Domain \\\n", | |
| "Company_Name \n", | |
| "Company201 0.0 0.000 \n", | |
| "Company239 0.0 0.000 \n", | |
| "Company138 0.0 9.375 \n", | |
| "Company453 0.0 0.000 \n", | |
| "Company223 0.0 0.000 \n", | |
| "\n", | |
| " ... \\\n", | |
| "Company_Name ... \n", | |
| "Company201 ... \n", | |
| "Company239 ... \n", | |
| "Company138 ... \n", | |
| "Company453 ... \n", | |
| "Company223 ... \n", | |
| "\n", | |
| " Linear or Non-linear business model_Non-Linear \\\n", | |
| "Company_Name \n", | |
| "Company201 1 \n", | |
| "Company239 1 \n", | |
| "Company138 1 \n", | |
| "Company453 0 \n", | |
| "Company223 1 \n", | |
| "\n", | |
| " Number of of Partners of company_Few \\\n", | |
| "Company_Name \n", | |
| "Company201 1 \n", | |
| "Company239 0 \n", | |
| "Company138 1 \n", | |
| "Company453 0 \n", | |
| "Company223 0 \n", | |
| "\n", | |
| " Number of of Partners of company_Many \\\n", | |
| "Company_Name \n", | |
| "Company201 0 \n", | |
| "Company239 0 \n", | |
| "Company138 0 \n", | |
| "Company453 0 \n", | |
| "Company223 0 \n", | |
| "\n", | |
| " Number of of Partners of company_No Info \\\n", | |
| "Company_Name \n", | |
| "Company201 0 \n", | |
| "Company239 0 \n", | |
| "Company138 0 \n", | |
| "Company453 1 \n", | |
| "Company223 0 \n", | |
| "\n", | |
| " Number of of Partners of company_None \\\n", | |
| "Company_Name \n", | |
| "Company201 0 \n", | |
| "Company239 1 \n", | |
| "Company138 0 \n", | |
| "Company453 0 \n", | |
| "Company223 1 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_High \\\n", | |
| "Company_Name \n", | |
| "Company201 0 \n", | |
| "Company239 0 \n", | |
| "Company138 1 \n", | |
| "Company453 0 \n", | |
| "Company223 0 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_Low \\\n", | |
| "Company_Name \n", | |
| "Company201 0 \n", | |
| "Company239 1 \n", | |
| "Company138 0 \n", | |
| "Company453 0 \n", | |
| "Company223 1 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_Medium \\\n", | |
| "Company_Name \n", | |
| "Company201 1 \n", | |
| "Company239 0 \n", | |
| "Company138 0 \n", | |
| "Company453 0 \n", | |
| "Company223 0 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_No Info \\\n", | |
| "Company_Name \n", | |
| "Company201 0 \n", | |
| "Company239 0 \n", | |
| "Company138 0 \n", | |
| "Company453 1 \n", | |
| "Company223 0 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_None \n", | |
| "Company_Name \n", | |
| "Company201 0 \n", | |
| "Company239 0 \n", | |
| "Company138 0 \n", | |
| "Company453 0 \n", | |
| "Company223 0 \n", | |
| "\n", | |
| "[5 rows x 367 columns]" | |
| ] | |
| }, | |
| "execution_count": 225, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "X_train.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 226, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "Company_Name\n", | |
| "Company201 1\n", | |
| "Company239 1\n", | |
| "Company138 0\n", | |
| "Company453 0\n", | |
| "Company223 1\n", | |
| "Name: Dependent-Company Status, dtype: int64" | |
| ] | |
| }, | |
| "execution_count": 226, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "y_train.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Quick training data exploration" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 296, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import matplotlib.pyplot as plt\n", | |
| "%matplotlib inline\n", | |
| "import seaborn as sb\n", | |
| "sb.set()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 247, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x7f98e6e8c5d0>" | |
| ] | |
| }, | |
| "execution_count": 247, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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HHOgc/7777tvv8XH66afnW9/61hHdXweyZ79f7YMf/OBrfq1/uPvo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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f98e6e79950>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "X_train[[c for c in NUMERIC_COLS.keys() if c not in inf_cols]].plot(kind='box', figsize=[20, 10])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 248, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Time to 1st investment (in months)</th>\n", | |
| " <th>Years of education</th>\n", | |
| " <th>Percent_skill_Product Management</th>\n", | |
| " <th>Number of of repeat investors</th>\n", | |
| " <th>Industry trend in investing</th>\n", | |
| " <th>Renown score</th>\n", | |
| " <th>Number of Co-founders</th>\n", | |
| " <th>Percent_skill_Consulting</th>\n", | |
| " <th>Percent_skill_Sales</th>\n", | |
| " <th>Percent_skill_Domain</th>\n", | |
| " <th>...</th>\n", | |
| " <th>Linear or Non-linear business model_Non-Linear</th>\n", | |
| " <th>Number of of Partners of company_Few</th>\n", | |
| " <th>Number of of Partners of company_Many</th>\n", | |
| " <th>Number of of Partners of company_No Info</th>\n", | |
| " <th>Number of of Partners of company_None</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_High</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_Low</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_Medium</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_No Info</th>\n", | |
| " <th>Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_None</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>count</th>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>...</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " <td>354.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>mean</th>\n", | |
| " <td>11.550847</td>\n", | |
| " <td>15.878531</td>\n", | |
| " <td>2.860145</td>\n", | |
| " <td>0.564972</td>\n", | |
| " <td>2.316384</td>\n", | |
| " <td>2.903955</td>\n", | |
| " <td>1.884181</td>\n", | |
| " <td>0.449169</td>\n", | |
| " <td>2.968618</td>\n", | |
| " <td>4.217476</td>\n", | |
| " <td>...</td>\n", | |
| " <td>0.672316</td>\n", | |
| " <td>0.141243</td>\n", | |
| " <td>0.025424</td>\n", | |
| " <td>0.220339</td>\n", | |
| " <td>0.612994</td>\n", | |
| " <td>0.064972</td>\n", | |
| " <td>0.511299</td>\n", | |
| " <td>0.166667</td>\n", | |
| " <td>0.177966</td>\n", | |
| " <td>0.079096</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>std</th>\n", | |
| " <td>16.647962</td>\n", | |
| " <td>8.390906</td>\n", | |
| " <td>4.554673</td>\n", | |
| " <td>1.203166</td>\n", | |
| " <td>1.359642</td>\n", | |
| " <td>2.890217</td>\n", | |
| " <td>1.166399</td>\n", | |
| " <td>2.157175</td>\n", | |
| " <td>5.314433</td>\n", | |
| " <td>7.711089</td>\n", | |
| " <td>...</td>\n", | |
| " <td>0.470033</td>\n", | |
| " <td>0.348765</td>\n", | |
| " <td>0.157631</td>\n", | |
| " <td>0.415062</td>\n", | |
| " <td>0.487754</td>\n", | |
| " <td>0.246825</td>\n", | |
| " <td>0.500580</td>\n", | |
| " <td>0.373205</td>\n", | |
| " <td>0.383026</td>\n", | |
| " <td>0.270271</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>min</th>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>...</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>25%</th>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>18.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>2.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>...</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>50%</th>\n", | |
| " <td>6.000000</td>\n", | |
| " <td>18.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>2.000000</td>\n", | |
| " <td>2.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>75%</th>\n", | |
| " <td>15.000000</td>\n", | |
| " <td>21.000000</td>\n", | |
| " <td>5.554688</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>3.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>5.554688</td>\n", | |
| " <td>5.554688</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " <td>0.000000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>max</th>\n", | |
| " <td>120.000000</td>\n", | |
| " <td>25.000000</td>\n", | |
| " <td>25.000000</td>\n", | |
| " <td>10.000000</td>\n", | |
| " <td>5.000000</td>\n", | |
| " <td>11.000000</td>\n", | |
| " <td>7.000000</td>\n", | |
| " <td>20.000000</td>\n", | |
| " <td>33.343750</td>\n", | |
| " <td>44.437500</td>\n", | |
| " <td>...</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " <td>1.000000</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "<p>8 rows × 367 columns</p>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " Time to 1st investment (in months) Years of education \\\n", | |
| "count 354.000000 354.000000 \n", | |
| "mean 11.550847 15.878531 \n", | |
| "std 16.647962 8.390906 \n", | |
| "min 0.000000 0.000000 \n", | |
| "25% 1.000000 18.000000 \n", | |
| "50% 6.000000 18.000000 \n", | |
| "75% 15.000000 21.000000 \n", | |
| "max 120.000000 25.000000 \n", | |
| "\n", | |
| " Percent_skill_Product Management Number of of repeat investors \\\n", | |
| "count 354.000000 354.000000 \n", | |
| "mean 2.860145 0.564972 \n", | |
| "std 4.554673 1.203166 \n", | |
| "min 0.000000 0.000000 \n", | |
| "25% 0.000000 0.000000 \n", | |
| "50% 0.000000 0.000000 \n", | |
| "75% 5.554688 1.000000 \n", | |
| "max 25.000000 10.000000 \n", | |
| "\n", | |
| " Industry trend in investing Renown score Number of Co-founders \\\n", | |
| "count 354.000000 354.000000 354.000000 \n", | |
| "mean 2.316384 2.903955 1.884181 \n", | |
| "std 1.359642 2.890217 1.166399 \n", | |
| "min 0.000000 0.000000 0.000000 \n", | |
| "25% 2.000000 0.000000 1.000000 \n", | |
| "50% 3.000000 2.000000 2.000000 \n", | |
| "75% 3.000000 5.000000 3.000000 \n", | |
| "max 5.000000 11.000000 7.000000 \n", | |
| "\n", | |
| " Percent_skill_Consulting Percent_skill_Sales Percent_skill_Domain \\\n", | |
| "count 354.000000 354.000000 354.000000 \n", | |
| "mean 0.449169 2.968618 4.217476 \n", | |
| "std 2.157175 5.314433 7.711089 \n", | |
| "min 0.000000 0.000000 0.000000 \n", | |
| "25% 0.000000 0.000000 0.000000 \n", | |
| "50% 0.000000 0.000000 0.000000 \n", | |
| "75% 0.000000 5.554688 5.554688 \n", | |
| "max 20.000000 33.343750 44.437500 \n", | |
| "\n", | |
| " ... \\\n", | |
| "count ... \n", | |
| "mean ... \n", | |
| "std ... \n", | |
| "min ... \n", | |
| "25% ... \n", | |
| "50% ... \n", | |
| "75% ... \n", | |
| "max ... \n", | |
| "\n", | |
| " Linear or Non-linear business model_Non-Linear \\\n", | |
| "count 354.000000 \n", | |
| "mean 0.672316 \n", | |
| "std 0.470033 \n", | |
| "min 0.000000 \n", | |
| "25% 0.000000 \n", | |
| "50% 1.000000 \n", | |
| "75% 1.000000 \n", | |
| "max 1.000000 \n", | |
| "\n", | |
| " Number of of Partners of company_Few \\\n", | |
| "count 354.000000 \n", | |
| "mean 0.141243 \n", | |
| "std 0.348765 \n", | |
| "min 0.000000 \n", | |
| "25% 0.000000 \n", | |
| "50% 0.000000 \n", | |
| "75% 0.000000 \n", | |
| "max 1.000000 \n", | |
| "\n", | |
| " Number of of Partners of company_Many \\\n", | |
| "count 354.000000 \n", | |
| "mean 0.025424 \n", | |
| "std 0.157631 \n", | |
| "min 0.000000 \n", | |
| "25% 0.000000 \n", | |
| "50% 0.000000 \n", | |
| "75% 0.000000 \n", | |
| "max 1.000000 \n", | |
| "\n", | |
| " Number of of Partners of company_No Info \\\n", | |
| "count 354.000000 \n", | |
| "mean 0.220339 \n", | |
| "std 0.415062 \n", | |
| "min 0.000000 \n", | |
| "25% 0.000000 \n", | |
| "50% 0.000000 \n", | |
| "75% 0.000000 \n", | |
| "max 1.000000 \n", | |
| "\n", | |
| " Number of of Partners of company_None \\\n", | |
| "count 354.000000 \n", | |
| "mean 0.612994 \n", | |
| "std 0.487754 \n", | |
| "min 0.000000 \n", | |
| "25% 0.000000 \n", | |
| "50% 1.000000 \n", | |
| "75% 1.000000 \n", | |
| "max 1.000000 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_High \\\n", | |
| "count 354.000000 \n", | |
| "mean 0.064972 \n", | |
| "std 0.246825 \n", | |
| "min 0.000000 \n", | |
| "25% 0.000000 \n", | |
| "50% 0.000000 \n", | |
| "75% 0.000000 \n", | |
| "max 1.000000 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_Low \\\n", | |
| "count 354.000000 \n", | |
| "mean 0.511299 \n", | |
| "std 0.500580 \n", | |
| "min 0.000000 \n", | |
| "25% 0.000000 \n", | |
| "50% 1.000000 \n", | |
| "75% 1.000000 \n", | |
| "max 1.000000 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_Medium \\\n", | |
| "count 354.000000 \n", | |
| "mean 0.166667 \n", | |
| "std 0.373205 \n", | |
| "min 0.000000 \n", | |
| "25% 0.000000 \n", | |
| "50% 0.000000 \n", | |
| "75% 0.000000 \n", | |
| "max 1.000000 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_No Info \\\n", | |
| "count 354.000000 \n", | |
| "mean 0.177966 \n", | |
| "std 0.383026 \n", | |
| "min 0.000000 \n", | |
| "25% 0.000000 \n", | |
| "50% 0.000000 \n", | |
| "75% 0.000000 \n", | |
| "max 1.000000 \n", | |
| "\n", | |
| " Top forums like 'Tech crunch' or 'Venture beat' talking about the company/model - How much is it being talked about?_None \n", | |
| "count 354.000000 \n", | |
| "mean 0.079096 \n", | |
| "std 0.270271 \n", | |
| "min 0.000000 \n", | |
| "25% 0.000000 \n", | |
| "50% 0.000000 \n", | |
| "75% 0.000000 \n", | |
| "max 1.000000 \n", | |
| "\n", | |
| "[8 rows x 367 columns]" | |
| ] | |
| }, | |
| "execution_count": 248, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "X_train.describe()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Modeling\n", | |
| "## Regularized logistic regression with cross-validation" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 345, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "from sklearn.linear_model import LogisticRegressionCV\n", | |
| "from sklearn.preprocessing import Normalizer, StandardScaler\n", | |
| "from sklearn.pipeline import Pipeline\n", | |
| "import sklearn\n", | |
| "from sklearn.metrics import accuracy_score, roc_auc_score, roc_curve, auc\n", | |
| "import pandas as pd" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 419, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "l1_logit_cv = LogisticRegressionCV(solver='liblinear', n_jobs=-1, cv=5, penalty='l1')\n", | |
| "l2_logit_cv = LogisticRegressionCV(solver='liblinear', n_jobs=-1, cv=5, penalty='l2')\n", | |
| "normalizer = Normalizer()\n", | |
| "scaler = StandardScaler()\n", | |
| "base_pipe = [('scaler', scaler), ('normalizer', normalizer)]\n", | |
| "l1_logit_cv_pipe = Pipeline(base_pipe+[('l1_logit', sklearn.clone(l1_logit_cv))])\n", | |
| "l2_logit_cv_pipe = Pipeline(base_pipe+[('l2_logit', sklearn.clone(l2_logit_cv))])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## l1\n", | |
| "### non-scaled inputs" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 420, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# fit\n", | |
| "l1_logit_cv = l1_logit_cv.fit(X_train.values, y_train.values) # scaling\n", | |
| "# accuracy\n", | |
| "l1_cv_train_acc = l1_logit_cv.score(X_train.values, y_train.values)\n", | |
| "l1_cv_test_acc = l1_logit_cv.score(X_test.values, y_test.values)\n", | |
| "# roc auc\n", | |
| "l1_cv_train_roc_auc = roc_auc_score(y_true=y_train.values, y_score=l1_logit_cv.predict(X_train.values))\n", | |
| "l1_cv_test_roc_auc = roc_auc_score(y_true=y_test.values, y_score=l1_logit_cv.predict(X_test.values))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### scaled inputs" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 421, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# fit\n", | |
| "l1_logit_cv_pipe = l1_logit_cv_pipe.fit(X_train.values, y_train.values) # scaling\n", | |
| "# accuracy\n", | |
| "l1_cv_pipe_train_acc = l1_logit_cv_pipe.score(X_train.values, y_train.values)\n", | |
| "l1_cv_pipe_test_acc = l1_logit_cv_pipe.score(X_test.values, y_test.values)\n", | |
| "# roc auc\n", | |
| "l1_cv_pipe_train_roc_auc = roc_auc_score(y_true=y_train.values, y_score=l1_logit_cv_pipe.predict(X_train.values))\n", | |
| "l1_cv_pipe_test_roc_auc = roc_auc_score(y_true=y_test.values, y_score=l1_logit_cv_pipe.predict(X_test.values))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## l2\n", | |
| "### non-scaled inputs" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 422, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# fit\n", | |
| "l2_logit_cv = l2_logit_cv.fit(X_train.values, y_train.values) # not scaled\n", | |
| "# accuracy\n", | |
| "l2_cv_train_acc = l2_logit_cv.score(X_train.values, y_train.values)\n", | |
| "l2_cv_test_acc = l2_logit_cv.score(X_test.values, y_test.values)\n", | |
| "# roc auc\n", | |
| "l2_cv_train_roc_auc = roc_auc_score(y_true=y_train.values, y_score=l2_logit_cv.predict(X_train.values))\n", | |
| "l2_cv_test_roc_auc = roc_auc_score(y_true=y_test.values, y_score=l2_logit_cv.predict(X_test.values))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### scaled inputs" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 423, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "# fit\n", | |
| "l2_logit_cv_pipe = l2_logit_cv_pipe.fit(X_train.values, y_train.values) # scaling\n", | |
| "# accuracy\n", | |
| "l2_cv_pipe_train_acc = l2_logit_cv_pipe.score(X_train.values, y_train.values)\n", | |
| "l2_cv_pipe_test_acc = l2_logit_cv_pipe.score(X_test.values, y_test.values)\n", | |
| "# roc auc\n", | |
| "l2_cv_pipe_train_roc_auc = roc_auc_score(y_true=y_train.values, y_score=l2_logit_cv_pipe.predict(X_train.values))\n", | |
| "l2_cv_pipe_test_roc_auc = roc_auc_score(y_true=y_test.values, y_score=l2_logit_cv_pipe.predict(X_test.values))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Best fit" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 436, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "acc = [l1_cv_train_acc, l1_cv_test_acc, l2_cv_train_acc, l2_cv_test_acc,\n", | |
| " l1_cv_pipe_train_acc, l1_cv_pipe_test_acc, l2_cv_pipe_train_acc, l2_cv_pipe_test_acc]\n", | |
| "roc_auc = [l1_cv_train_roc_auc, l1_cv_test_roc_auc, l2_cv_train_roc_auc, l2_cv_test_roc_auc,\n", | |
| " l1_cv_pipe_train_roc_auc, l1_cv_pipe_test_roc_auc, l2_cv_pipe_train_roc_auc, l2_cv_pipe_test_roc_auc]\n", | |
| "\n", | |
| "sets = ['train', 'test']*4\n", | |
| "logits = ['l1_logit']*2 + ['l2_logit']*2 + ['l1_logit_pipe']*2 + ['l2_logit_pipe']*2\n", | |
| "\n", | |
| "results_df = pd.DataFrame(dict(accuracy=acc, roc_auc=roc_auc, logits=logits, sets=sets))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 440, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "def add_numbers(ax):\n", | |
| " for p in ax.patches:\n", | |
| " height = p.get_height()\n", | |
| " ax.text(p.get_x()+p.get_width()/2.,\n", | |
| " height,\n", | |
| " '{:1.2f}'.format(height),\n", | |
| " ha=\"center\")\n", | |
| " \n", | |
| "def barplot(y, df, ax):\n", | |
| " sb.barplot(x='logits', y=y, hue='sets', data=df, ax=ax)\n", | |
| " ax.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,\n", | |
| " ncol=2, mode=\"expand\", borderaxespad=0.)\n", | |
| " add_numbers(ax)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 442, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f98e65d4bd0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "sb.set(font_scale=1.75)\n", | |
| "fig, axs = plt.subplots(1, 2, figsize=[20, 7])\n", | |
| "barplot('accuracy', results_df, axs[0])\n", | |
| "barplot('roc_auc', results_df, axs[1])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Best fit -> l1_logit_cv_pipe\n", | |
| "### ROC plot for best fit" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 429, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "logit_y_score = l1_logit_cv_pipe.predict(X_test.values)\n", | |
| "logit_fpr, logit_tpr, _ = roc_curve(y_test, logit_y_score)\n", | |
| "logit_roc_auc = auc(fpr, tpr)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 430, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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+fRxdpoiISJ6mMCkiIo+kcePGYTabWb16NZ9++inR0dH4+/vTr18/XnzxRdts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\nIYQQQohs+x8m5YdPbIYrewAAAABJRU5ErkJggg==\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f98d32cb650>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=[15, 7])\n", | |
| "lw = 2\n", | |
| "plt.plot(fpr, tpr, color='darkorange', lw=lw,\n", | |
| " label='ROC curve (area = {:.02f})'.format(logit_roc_auc))\n", | |
| "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n", | |
| "plt.xlim([0.0, 1.0])\n", | |
| "plt.ylim([0.0, 1.05])\n", | |
| "plt.xlabel('False Positive Rate')\n", | |
| "plt.ylabel('True Positive Rate')\n", | |
| "plt.title('Receiver operating characteristic example')\n", | |
| "plt.legend(loc=\"lower right\")\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Gradient boosted classification trees with cross-validation\n", | |
| "(because I am lazy to grid-search, using SMAC[1] to find hyper-parameters) \n", | |
| "[1] http://www.cs.ubc.ca/labs/beta/Projects/SMAC/, need to pip-install python wrapper from https://github.com/sfalkner/pySMAC" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 317, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import xgboost\n", | |
| "import pysmac\n", | |
| "from sklearn.model_selection import cross_val_score\n", | |
| "from functools import partial\n", | |
| "\n", | |
| "def _cv_score(estimator, X, y, scoring, cv, n_jobs, verbose, pre_dispatch, **params):\n", | |
| " \"\"\"\n", | |
| " Pickle-safe function for setting estimator parameters and runninc\n", | |
| " cross-validation used for hyperparameter optimization.\n", | |
| " \"\"\"\n", | |
| " estimator.set_params(**params)\n", | |
| " return - cross_val_score(estimator=estimator, X=X, y=y, scoring=scoring,\n", | |
| " cv=cv, n_jobs=n_jobs, verbose=verbose,\n", | |
| " pre_dispatch=pre_dispatch).mean()\n", | |
| "param_dists = {\n", | |
| " 'n_estimators': ('integer', [1, 1000], 100),\n", | |
| " \n", | |
| " 'max_delta_step': ('real', [0.1, 1.], 0.5),\n", | |
| " 'max_depth': ('integer', [1, 10], 5),\n", | |
| " 'min_child_weight': ('integer', [1, 10], 5),\n", | |
| " \n", | |
| " 'gamma': ('real', [0.01, 10.], 0.5),\n", | |
| " \n", | |
| " 'subsample': ('real', [.1, .9], 0.5),\n", | |
| " 'colsample_bylevel' :('real', [0.1, 0.9], 0.5),\n", | |
| " 'colsample_bytree' :('real', [0.1, 0.9], 0.5),\n", | |
| " \n", | |
| " 'reg_alpha': ('real', [0., 100.], 0.),\n", | |
| " 'reg_lambda': ('real', [0., 100.], 0.),\n", | |
| " \n", | |
| " 'learning_rate': ('real', [1e-5, 1.], 1e-1),\n", | |
| "}\n", | |
| "\n", | |
| "# hyper-parameter optimization uzing SMAC optimizer\n", | |
| "opt = pysmac.SMAC_optimizer()\n", | |
| "opt.smac_options['java_executable'] = 'java'# '/usr/bin/java -d32'\n", | |
| "\n", | |
| "strat_cv = sklearn.model_selection.StratifiedKFold(n_splits=5)\n", | |
| "\n", | |
| "xgb_clf = xgboost.XGBClassifier(silent=True, nthread=-1)\n", | |
| "\n", | |
| "cv_objective = partial(_cv_score, estimator=xgb_clf, X=X_train.values, y=y_train.values,\n", | |
| " scoring='neg_log_loss', cv=strat_cv, n_jobs=1,\n", | |
| " verbose=1, pre_dispatch='2*n_jobs')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 318, | |
| "metadata": { | |
| "collapsed": false, | |
| "scrolled": true | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.4s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.6s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.5s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.5s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.4s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.7s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.4s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.3s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.1s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 2.2s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.6s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 2.2s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.4s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.7s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 0.1s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.7s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.9s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.3s finished\n", | |
| "[Parallel(n_jobs=1)]: Done 5 out of 5 | elapsed: 1.5s finished\n", | |
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| ] | |
| } | |
| ], | |
| "source": [ | |
| "value, parameters = \\\n", | |
| " opt.minimize(cv_objective, # the function to be minimized\n", | |
| " 100, # the maximum number of evaluations\n", | |
| " param_dists,\n", | |
| " num_runs=1,\n", | |
| " num_procs=5)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 327, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "{'colsample_bylevel': 0.3732398142090083,\n", | |
| " 'colsample_bytree': 0.42691583166718006,\n", | |
| " 'gamma': 0.15175438536162458,\n", | |
| " 'learning_rate': 0.15060483902929975,\n", | |
| " 'max_delta_step': 0.5524967888544415,\n", | |
| " 'max_depth': 1,\n", | |
| " 'min_child_weight': 1,\n", | |
| " 'n_estimators': 554,\n", | |
| " 'reg_alpha': 1.8733695708081588,\n", | |
| " 'reg_lambda': 18.02124059035505,\n", | |
| " 'subsample': 0.8450620447893316}" | |
| ] | |
| }, | |
| "execution_count": 327, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "parameters" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 325, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "xgb_clf.set_params(**parameters)\n", | |
| "xgb_clf = xgb_clf.fit(X_train.values, y_train.values)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 326, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Train accuracy 0.95197740113\n", | |
| "Test accuracy 0.915254237288\n", | |
| "Train ROC AUC 0.946741854637\n", | |
| "Train ROC AUC 0.889452011403\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print 'Train accuracy', xgb_clf.score(X_train.values, y_train.values)\n", | |
| "print 'Test accuracy', xgb_clf.score(X_test.values, y_test.values)\n", | |
| "\n", | |
| "print 'Train ROC AUC', roc_auc_score(y_true=y_train.values, y_score=xgb_clf.predict(X_train.values))\n", | |
| "print 'Train ROC AUC', roc_auc_score(y_true=y_test.values, y_score=xgb_clf.predict(X_test.values))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 353, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "xgb_y_score = xgb_clf.predict(X_test.values)\n", | |
| "xgb_fpr, xgb_tpr, _ = roc_curve(y_test.values, xgb_y_score)\n", | |
| "xgb_roc_auc = auc(xgb_fpr, xgb_tpr)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 412, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
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84e4yRURE8jSFSRERuS+99dZbmM1mVqxYwQcffEBiYiJFihShd+/ePPvss/bZ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\naNMmwuSEIiIi8ktXLZNvvvkmhmFcdt8//vEP4GKxNAwDi8WiMlmB+X3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fNDuOSIW2YMFO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\nXCdjD6rsjgmRF3l57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FIIIYQQQgghRK5JMimEEEIIIYQQItckmRRCCCGEEEIIkWuSTAoh\nhBBCCCGEyDVJJoUQQgghhBBC5Jokk0IIIYQQQgghck2SSSGEEEIIIYQQufZ/fPR4dE1BiL4AAAAA\nSUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f98d2f11810>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plt.figure(figsize=[15, 7])\n", | |
| "lw = 2\n", | |
| "plt.plot(xgb_fpr, xgb_tpr, color='darkorange',\n", | |
| " lw=lw, label='ROC curve (area = {:.02f})'.format(xgb_roc_auc))\n", | |
| "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n", | |
| "plt.xlim([0.0, 1.0])\n", | |
| "plt.ylim([0.0, 1.05])\n", | |
| "plt.xlabel('False Positive Rate')\n", | |
| "plt.ylabel('True Positive Rate')\n", | |
| "plt.title('Receiver operating characteristic example')\n", | |
| "plt.legend(loc=\"lower right\")\n", | |
| "plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Conclusion: xgb_clf better generalization than l1_logit_cv_pipe" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Further work might inlcude: \n", | |
| " * modify and include dumped cols\n", | |
| " * try smarter missing values handling (instead of 0, use either previous/next value or interpolated values or KNN imputatoin)\n", | |
| " * try feature engineering by exploring data distributions in more detailed way\n", | |
| " * dimensionality reduction using PCA/KPCA or T-SNE\n", | |
| " * different model, e.g. SVC" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 2", | |
| "language": "python", | |
| "name": "python2" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 2 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython2", | |
| "version": "2.7.6" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 1 | |
| } |
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| BOOL_COLS = { | |
| 'Dependent-Company Status': str, # to bool | |
| 'Has the team size grown': str, # to bool | |
| 'Presence of a top angel or venture fund in previous round of investment': str, # to bool | |
| 'Worked in top companies': str, # to bool | |
| 'Have been part of startups in the past?': str, # to bool | |
| 'Have been part of successful startups in the past?': str, # to bool | |
| 'Was he or she partner in Big 5 consulting?': str, # to bool | |
| 'Consulting experience?': str, # to bool | |
| 'Focus on consumer data?': str, # to bool | |
| 'Subscription based business': str, # to bool | |
| 'Capital intensive business e.g. e-commerce, Engineering products and operations can also \ | |
| cause a business to be capital intensive': str, # to bool | |
| 'Crowdsourcing based business': str, # to bool | |
| 'Crowdfunding based business': str, # to bool | |
| 'Machine Learning based business': str, # to bool | |
| 'Predictive Analytics business': str, # to bool | |
| 'Speech analytics business': str, # to bool | |
| 'Prescriptive analytics business': str, # to bool | |
| 'Big Data Business': str, # to bool | |
| 'Cross-Channel Analytics/ marketing channels': str, # to bool | |
| 'Owns data or not? (monetization of data) e.g. Factual': str, # to bool | |
| 'Is the company an aggregator/market place? e.g. Bluekai': str, # to bool | |
| 'Exposure across the globe': str, # to bool | |
| 'Relevance of education to venture': str, # to bool | |
| 'Relevance of experience to venture': str, # to bool | |
| 'Pricing Strategy': str, # to bool | |
| 'Hyper localisation': str, # to bool | |
| 'Long term relationship with other founders': str, # to bool | |
| 'Proprietary or patent position (competitive position)': str, # to bool | |
| 'Barriers of entry for the competitors': str, # to bool | |
| 'Company awards': str, # to bool | |
| 'Controversial history of founder or co founder': str, # to bool | |
| 'Legal risk and intellectual property': str, # to bool | |
| 'Technical proficiencies to analyse and interpret unstructured data': str, # to bool | |
| 'Solutions offered': str, # to bool | |
| 'Invested through global incubation competitions?': str, # to bool | |
| } | |
| DATETIME_COLS = { | |
| 'Est. Founding Date': str, # to datetime | |
| 'Last Funding Date': str, # to datetime and number of days | |
| } | |
| CATEGORY_COLS = { | |
| 'year of founding': int, | |
| 'Industry of company': str, # to category | |
| 'Country of company': str, # to category | |
| 'Continent of company': str, # to category | |
| 'Number of Sales Support material': str, # to category | |
| 'Average size of companies worked for in the past': str, # to category | |
| 'Product or service company?': str, # to category | |
| 'Focus on structured or unstructured data': str, # to category | |
| 'Catering to product/service across verticals': str, # to category | |
| 'Focus on private or public data?': str, # to category | |
| 'Cloud or platform based serive/product?': str, # to category | |
| 'Local or global player': str, # to category | |
| 'Linear or Non-linear business model': str, # to category | |
| 'Number of of Partners of company': str, # to category | |
| 'Online or offline venture - physical location based business or online venture?': str, # to category | |
| 'B2C or B2B venture?': str, # to category | |
| "Top forums like 'Tech crunch' or 'Venture beat' \ | |
| talking about the company/model - How much is it being talked about?": str, # to category | |
| 'Average Years of experience for founder and co founder': str, # to category | |
| 'Breadth of experience across verticals': str, # to category | |
| 'Highest education': str, # to category | |
| 'Specialization of highest education': str, # to category | |
| 'Degree from a Tier 1 or Tier 2 university?': str, # to category | |
| 'Renowned in professional circle': str, # to category | |
| 'Experience in selling and building products': str, # to category | |
| 'Top management similarity': str, # to category | |
| 'Number of of Research publications': str, # to category | |
| 'Team Composition score': str, # to category | |
| 'Dificulty of Obtaining Work force': str, # to category | |
| 'Time to market service or product': str, # to category | |
| 'Employee benefits and salary structures': str, # to category | |
| 'Client Reputation': str, # to category | |
| 'Disruptiveness of technology': str, # to category | |
| 'Survival through recession, based on existence of the \ | |
| company through recession times': str, # to category | |
| 'Gartner hype cycle stage': str, # to category | |
| 'Time to maturity of technology (in years)': str, # to category | |
| } | |
| CATEGORY_COLS = dict.fromkeys(CATEGORY_COLS.keys(), 'category') | |
| INDEX_COL = { | |
| 'Company_Name': str, # to index | |
| } | |
| NUMERIC_COLS = { | |
| 'Age of company in years': int, | |
| 'Internet Activity Score': float, | |
| # 'Short Description of company profile': str, | |
| # 'Focus functions of company': str, | |
| # 'Investors': str, | |
| 'Employee Count': int, | |
| 'Employees count MoM change': float, | |
| 'Last Funding Amount': float, | |
| 'Number of Investors in Seed': int, | |
| 'Number of Investors in Angel and or VC': int, | |
| 'Number of Co-founders': int, | |
| 'Number of of advisors': int, | |
| 'Team size Senior leadership': int, | |
| 'Team size all employees': int, | |
| 'Number of of repeat investors': int, | |
| 'Experience in Fortune 100 organizations': int, | |
| 'Experience in Fortune 500 organizations': int, | |
| 'Experience in Fortune 1000 organizations': int, | |
| 'Years of education': int, | |
| 'Number of Recognitions for Founders and Co-founders': int, | |
| 'Skills score': float, | |
| 'google page rank of company website': int, | |
| 'Industry trend in investing': float, | |
| 'Number of Direct competitors': int, | |
| 'Employees per year of company existence': float, | |
| 'Last round of funding received (in milionUSD)': float, | |
| 'Time to 1st investment (in months)': int, | |
| 'Avg time to investment - average across all rounds, measured from previous investment': float, | |
| 'Percent_skill_Entrepreneurship': float, | |
| 'Percent_skill_Operations': float, | |
| 'Percent_skill_Engineering': float, | |
| 'Percent_skill_Marketing': float, | |
| 'Percent_skill_Leadership': float, | |
| 'Percent_skill_Data Science': float, | |
| 'Percent_skill_Business Strategy': float, | |
| 'Percent_skill_Product Management': float, | |
| 'Percent_skill_Sales': float, | |
| 'Percent_skill_Domain': float, | |
| 'Percent_skill_Law': float, | |
| 'Percent_skill_Consulting': float, | |
| 'Percent_skill_Finance': float, | |
| 'Percent_skill_Investment': float, | |
| 'Renown score': int | |
| } | |
| ALL_COLS = dict(INDEX_COL.items() + | |
| NUMERIC_COLS.items() + | |
| DATETIME_COLS.items() + | |
| BOOL_COLS.items() + | |
| CATEGORY_COLS.items()) |
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