US Charities | Machine Learning with Decision Trees and Random Forests.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": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "      <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>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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<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.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Animals</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",
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       "      <td>-0.263579</td>\n",
       "      <td>-0.186773</td>\n",
       "      <td>-0.866057</td>\n",
       "      <td>-0.181814</td>\n",
       "      <td>-0.047114</td>\n",
       "      <td>0.462148</td>\n",
       "      <td>-0.156168</td>\n",
       "      <td>-0.176026</td>\n",
       "      <td>-0.146236</td>\n",
       "      <td>0.330661</td>\n",
       "      <td>...</td>\n",
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       "      <td>-0.327716</td>\n",
       "      <td>-0.285942</td>\n",
       "      <td>-0.230243</td>\n",
       "      <td>-0.334349</td>\n",
       "      <td>-0.635888</td>\n",
       "      <td>4.886773</td>\n",
       "      <td>-0.270780</td>\n",
       "      <td>-0.229912</td>\n",
       "      <td>-0.157289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.601692</td>\n",
       "      <td>-0.048412</td>\n",
       "      <td>-0.711788</td>\n",
       "      <td>-0.869707</td>\n",
       "      <td>0.022858</td>\n",
       "      <td>-0.004671</td>\n",
       "      <td>-0.165434</td>\n",
       "      <td>-0.099415</td>\n",
       "      <td>-0.103574</td>\n",
       "      <td>-0.157966</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.417255</td>\n",
       "      <td>-0.327716</td>\n",
       "      <td>3.497212</td>\n",
       "      <td>-0.230243</td>\n",
       "      <td>-0.334349</td>\n",
       "      <td>-0.635888</td>\n",
       "      <td>-0.204634</td>\n",
       "      <td>-0.270780</td>\n",
       "      <td>-0.229912</td>\n",
       "      <td>-0.157289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.714396</td>\n",
       "      <td>-0.198450</td>\n",
       "      <td>-0.623248</td>\n",
       "      <td>-1.071041</td>\n",
       "      <td>-0.115813</td>\n",
       "      <td>-0.836696</td>\n",
       "      <td>-0.133811</td>\n",
       "      <td>-0.188129</td>\n",
       "      <td>-0.152597</td>\n",
       "      <td>-0.799611</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.417255</td>\n",
       "      <td>-0.327716</td>\n",
       "      <td>-0.285942</td>\n",
       "      <td>-0.230243</td>\n",
       "      <td>-0.334349</td>\n",
       "      <td>-0.635888</td>\n",
       "      <td>-0.204634</td>\n",
       "      <td>3.693041</td>\n",
       "      <td>-0.229912</td>\n",
       "      <td>-0.157289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.601692</td>\n",
       "      <td>-0.156641</td>\n",
       "      <td>-0.806334</td>\n",
       "      <td>-1.159125</td>\n",
       "      <td>0.081865</td>\n",
       "      <td>1.160365</td>\n",
       "      <td>-0.111714</td>\n",
       "      <td>-0.118328</td>\n",
       "      <td>-0.163070</td>\n",
       "      <td>0.355093</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.417255</td>\n",
       "      <td>-0.327716</td>\n",
       "      <td>3.497212</td>\n",
       "      <td>-0.230243</td>\n",
       "      <td>-0.334349</td>\n",
       "      <td>-0.635888</td>\n",
       "      <td>-0.204634</td>\n",
       "      <td>-0.270780</td>\n",
       "      <td>-0.229912</td>\n",
       "      <td>-0.157289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.638054</td>\n",
       "      <td>-0.191633</td>\n",
       "      <td>-0.706060</td>\n",
       "      <td>0.782913</td>\n",
       "      <td>-0.085533</td>\n",
       "      <td>0.112034</td>\n",
       "      <td>-0.160089</td>\n",
       "      <td>-0.179602</td>\n",
       "      <td>-0.148440</td>\n",
       "      <td>0.442531</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.417255</td>\n",
       "      <td>-0.327716</td>\n",
       "      <td>-0.285942</td>\n",
       "      <td>-0.230243</td>\n",
       "      <td>-0.334349</td>\n",
       "      <td>-0.635888</td>\n",
       "      <td>-0.204634</td>\n",
       "      <td>-0.270780</td>\n",
       "      <td>4.349486</td>\n",
       "      <td>-0.157289</td>\n",
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      ],
      "text/plain": [
       "   accountability_score  administrative_expenses  \\\n",
       "0             -0.263579                -0.186773   \n",
       "1             -0.601692                -0.048412   \n",
       "2             -0.714396                -0.198450   \n",
       "3             -0.601692                -0.156641   \n",
       "4              0.638054                -0.191633   \n",
       "\n",
       "   compensation_leader_compensation  compensation_leader_expense_percent  \\\n",
       "0                         -0.866057                            -0.181814   \n",
       "1                         -0.711788                            -0.869707   \n",
       "2                         -0.623248                            -1.071041   \n",
       "3                         -0.806334                            -1.159125   \n",
       "4                         -0.706060                             0.782913   \n",
       "\n",
       "   excess_or_deficit_for_year  financial_score  fundraising_expenses  \\\n",
       "0                   -0.047114         0.462148             -0.156168   \n",
       "1                    0.022858        -0.004671             -0.165434   \n",
       "2                   -0.115813        -0.836696             -0.133811   \n",
       "3                    0.081865         1.160365             -0.111714   \n",
       "4                   -0.085533         0.112034             -0.160089   \n",
       "\n",
       "   net_assets  other_revenue  overall_score    ...      Animals  \\\n",
       "0   -0.176026      -0.146236       0.330661    ...    -0.417255   \n",
       "1   -0.099415      -0.103574      -0.157966    ...    -0.417255   \n",
       "2   -0.188129      -0.152597      -0.799611    ...    -0.417255   \n",
       "3   -0.118328      -0.163070       0.355093    ...    -0.417255   \n",
       "4   -0.179602      -0.148440       0.442531    ...    -0.417255   \n",
       "\n",
       "   Arts, Culture, Humanities  Community Development  Education  Environment  \\\n",
       "0                  -0.327716              -0.285942  -0.230243    -0.334349   \n",
       "1                  -0.327716               3.497212  -0.230243    -0.334349   \n",
       "2                  -0.327716              -0.285942  -0.230243    -0.334349   \n",
       "3                  -0.327716               3.497212  -0.230243    -0.334349   \n",
       "4                  -0.327716              -0.285942  -0.230243    -0.334349   \n",
       "\n",
       "     Health  Human Services  Human and Civil Rights  International  Religion  \n",
       "0 -0.635888        4.886773               -0.270780      -0.229912 -0.157289  \n",
       "1 -0.635888       -0.204634               -0.270780      -0.229912 -0.157289  \n",
       "2 -0.635888       -0.204634                3.693041      -0.229912 -0.157289  \n",
       "3 -0.635888       -0.204634               -0.270780      -0.229912 -0.157289  \n",
       "4 -0.635888       -0.204634               -0.270780       4.349486 -0.157289  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "scaler.fit(comp_leader_df.drop('excess_or_deficit', axis = 1))\n",
    "scaled_features = scaler.transform(comp_leader_df.drop('excess_or_deficit', axis = 1))\n",
    "df_scaled_features = pd.DataFrame(scaled_features, columns = comp_leader_df.columns[:-1])\n",
    "df_scaled_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_scaled_features[['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', 'Education',\n",
    "                        'Environment', 'Health', 'Human Services',\n",
    "                        'Human and Civil Rights', 'International',\n",
    "                        'Religion']]\n",
    "y = comp_leader_df['excess_or_deficit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state = 101)\n",
    "dtree = DecisionTreeClassifier()\n",
    "dtree.fit(X_train, y_train)\n",
    "y_pred = dtree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2:  1.0\n",
      "MAE:  0.4698\n",
      "MSE:  0.4698\n",
      "RMSE: 0.6854\n"
     ]
    }
   ],
   "source": [
    "print('R^2: ', dtree.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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.45      0.46      0.46      1245\n",
      "        1.0       0.59      0.58      0.59      1671\n",
      "\n",
      "avg / total       0.53      0.53      0.53      2916\n",
      "\n",
      "[[576 669]\n",
      " [701 970]]\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test, y_pred))\n",
    "print(metrics.confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_estimators = 600)\n",
    "rfc.fit(X_train, y_train)\n",
    "y_pred = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       0.51      0.33      0.40      1245\n",
      "        1.0       0.60      0.76      0.67      1671\n",
      "\n",
      "avg / total       0.56      0.58      0.56      2916\n",
      "\n",
      "[[ 414  831]\n",
      " [ 402 1269]]\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test, y_pred))\n",
    "print(metrics.confusion_matrix(y_test, y_pred))"
   ]
  }
 ],
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