US Charities | Machine Learning with Logistic Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('charity_navigator_clean.csv', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 0\n",
    "for excess_or_deficit in df['excess_or_deficit_for_year']:\n",
    "    if df.loc[n, 'excess_or_deficit_for_year'] > 0:\n",
    "        df.loc[n, 'excess_or_deficit'] = 1\n",
    "        n += 1\n",
    "    elif df.loc[n, 'excess_or_deficit_for_year'] <= 0:\n",
    "        df.loc[n, 'excess_or_deficit'] = 0\n",
    "        n += 1\n",
    "    else:\n",
    "        n += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accountability_score</th>\n",
       "      <th>administrative_expenses</th>\n",
       "      <th>compensation_leader_compensation</th>\n",
       "      <th>compensation_leader_expense_percent</th>\n",
       "      <th>excess_or_deficit_for_year</th>\n",
       "      <th>financial_score</th>\n",
       "      <th>fundraising_expenses</th>\n",
       "      <th>net_assets</th>\n",
       "      <th>other_revenue</th>\n",
       "      <th>overall_score</th>\n",
       "      <th>...</th>\n",
       "      <th>Arts, Culture, Humanities</th>\n",
       "      <th>Community Development</th>\n",
       "      <th>Education</th>\n",
       "      <th>Environment</th>\n",
       "      <th>Health</th>\n",
       "      <th>Human Services</th>\n",
       "      <th>Human and Civil Rights</th>\n",
       "      <th>International</th>\n",
       "      <th>Religion</th>\n",
       "      <th>Research and Public Policy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>89.0</td>\n",
       "      <td>164590.0</td>\n",
       "      <td>53463.0</td>\n",
       "      <td>3.11</td>\n",
       "      <td>349718.0</td>\n",
       "      <td>90.56</td>\n",
       "      <td>111522</td>\n",
       "      <td>1350382</td>\n",
       "      <td>49634.0</td>\n",
       "      <td>89.75</td>\n",
       "      <td>...</td>\n",
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       "      <th>1</th>\n",
       "      <td>86.0</td>\n",
       "      <td>1001560.0</td>\n",
       "      <td>73500.0</td>\n",
       "      <td>1.47</td>\n",
       "      <td>1175965.0</td>\n",
       "      <td>85.92</td>\n",
       "      <td>54613</td>\n",
       "      <td>14773920</td>\n",
       "      <td>382540.0</td>\n",
       "      <td>85.95</td>\n",
       "      <td>...</td>\n",
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       "      <th>2</th>\n",
       "      <td>85.0</td>\n",
       "      <td>93957.0</td>\n",
       "      <td>85000.0</td>\n",
       "      <td>0.99</td>\n",
       "      <td>-461502.0</td>\n",
       "      <td>77.65</td>\n",
       "      <td>248833</td>\n",
       "      <td>-770370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.96</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>86.0</td>\n",
       "      <td>346867.0</td>\n",
       "      <td>61220.0</td>\n",
       "      <td>0.78</td>\n",
       "      <td>1872733.0</td>\n",
       "      <td>97.50</td>\n",
       "      <td>384550</td>\n",
       "      <td>11460087</td>\n",
       "      <td>-81726.0</td>\n",
       "      <td>89.94</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>97.0</td>\n",
       "      <td>135195.0</td>\n",
       "      <td>74244.0</td>\n",
       "      <td>5.41</td>\n",
       "      <td>-103940.0</td>\n",
       "      <td>87.08</td>\n",
       "      <td>87436</td>\n",
       "      <td>723772</td>\n",
       "      <td>32436.0</td>\n",
       "      <td>90.62</td>\n",
       "      <td>...</td>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   accountability_score  administrative_expenses  \\\n",
       "0                  89.0                 164590.0   \n",
       "1                  86.0                1001560.0   \n",
       "2                  85.0                  93957.0   \n",
       "3                  86.0                 346867.0   \n",
       "4                  97.0                 135195.0   \n",
       "\n",
       "   compensation_leader_compensation  compensation_leader_expense_percent  \\\n",
       "0                           53463.0                                 3.11   \n",
       "1                           73500.0                                 1.47   \n",
       "2                           85000.0                                 0.99   \n",
       "3                           61220.0                                 0.78   \n",
       "4                           74244.0                                 5.41   \n",
       "\n",
       "   excess_or_deficit_for_year  financial_score  fundraising_expenses  \\\n",
       "0                    349718.0            90.56                111522   \n",
       "1                   1175965.0            85.92                 54613   \n",
       "2                   -461502.0            77.65                248833   \n",
       "3                   1872733.0            97.50                384550   \n",
       "4                   -103940.0            87.08                 87436   \n",
       "\n",
       "   net_assets  other_revenue  overall_score             ...              \\\n",
       "0     1350382        49634.0          89.75             ...               \n",
       "1    14773920       382540.0          85.95             ...               \n",
       "2     -770370            0.0          80.96             ...               \n",
       "3    11460087       -81726.0          89.94             ...               \n",
       "4      723772        32436.0          90.62             ...               \n",
       "\n",
       "   Arts, Culture, Humanities  Community Development  Education  Environment  \\\n",
       "0                          0                      0          0            0   \n",
       "1                          0                      0          1            0   \n",
       "2                          0                      0          0            0   \n",
       "3                          0                      0          1            0   \n",
       "4                          0                      0          0            0   \n",
       "\n",
       "   Health  Human Services  Human and Civil Rights  International  Religion  \\\n",
       "0       0               0                       1              0         0   \n",
       "1       0               0                       0              0         0   \n",
       "2       0               0                       0              1         0   \n",
       "3       0               0                       0              0         0   \n",
       "4       0               0                       0              0         1   \n",
       "\n",
       "   Research and Public Policy  \n",
       "0                           0  \n",
       "1                           0  \n",
       "2                           0  \n",
       "3                           0  \n",
       "4                           0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp_leader_df = df[df['compensation_leader_compensation'].isnull() == False]\n",
    "org_type_dummies = pd.get_dummies(comp_leader_df.org_type)\n",
    "comp_leader_df = pd.concat([comp_leader_df, org_type_dummies], axis=1, join = 'outer')\n",
    "comp_leader_df.drop(['charity_name', 'charity_url', \n",
    "                     'city', 'cn_advisory', \n",
    "                     'state', 'org_type',\n",
    "                     'org_category', 'compensation_leader_title'], \n",
    "                    axis = 1, inplace = True)\n",
    "comp_leader_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = comp_leader_df[['accountability_score', 'financial_score',\n",
    "                    'overall_score', 'administrative_expenses',\n",
    "                    'fundraising_expenses', 'net_assets',\n",
    "                    'other_revenue', 'payments_to_affiliates',\n",
    "                    'compensation_leader_compensation', \n",
    "                    'compensation_leader_expense_percent',\n",
    "                    'Animals', 'Arts, Culture, Humanities',\n",
    "                    'Community Development',\n",
    "                    'Education', 'Environment',\n",
    "                    'Health', 'Human Services',\n",
    "                    'Human and Civil Rights', 'International',\n",
    "                    'Religion', 'Research and Public Policy']]\n",
    "y = comp_leader_df['excess_or_deficit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state = 101)\n",
    "logmodel = LogisticRegression()\n",
    "logmodel.fit(X_train, y_train)\n",
    "y_pred = logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2:  0.5706\n",
      "MAE:  0.4294\n",
      "MSE:  0.4294\n",
      "RMSE: 0.6553\n"
     ]
    }
   ],
   "source": [
    "print('R^2: ', logmodel.score(X_train, y_train).round(4))\n",
    "print('MAE: ', metrics.mean_absolute_error(y_test, y_pred).round(4))\n",
    "print('MSE: ', metrics.mean_squared_error(y_test, y_pred).round(4))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)).round(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.23      0.00      0.00      1245\n",
      "        1.0       0.57      0.99      0.73      1671\n",
      "\n",
      "avg / total       0.43      0.57      0.42      2916\n",
      "\n",
      "[[   3 1242]\n",
      " [  10 1661]]\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test, y_pred))\n",
    "print(metrics.confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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