US Charities | Machine Learning with Linear 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": [
    {
     "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",
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       "      <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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       "      <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",
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       "    </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",
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       "      <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",
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       "<p>5 rows × 24 columns</p>\n",
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      "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 24 columns]"
      ]
     },
     "execution_count": 3,
     "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = comp_leader_df[['accountability_score', 'financial_score',\n",
    "                    'overall_score', 'administrative_expenses',\n",
    "                    'excess_or_deficit_for_year', 'fundraising_expenses',\n",
    "                    'net_assets', 'other_revenue',\n",
    "                    'payments_to_affiliates', 'Animals',\n",
    "                    'Arts, Culture, Humanities', '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['compensation_leader_compensation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/charlieyaris/.venvs/lpthw/lib/python3.6/site-packages/scipy/linalg/basic.py:1226: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n",
      "  warnings.warn(mesg, RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state = 101)\n",
    "linreg = LinearRegression()\n",
    "linreg.fit(X_train, y_train)\n",
    "y_pred = linreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/charlieyaris/.venvs/lpthw/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.jointplot(x = y_test, y = y_pred, \n",
    "                   kind = 'scatter');\n",
    "ax.set_axis_labels('Y Test', 'Predicted Y')\n",
    "ax.plot(sns.regplot, sns.distplot);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/charlieyaris/.venvs/lpthw/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.distplot((y_test - y_pred), bins = 50)\n",
    "ax.xaxis.set_tick_params(which = 'both', rotation = 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " -49257.38270579724\n",
      "                              Coeficient\n",
      "accountability_score         1826.351362\n",
      "financial_score               639.494968\n",
      "overall_score                -143.684141\n",
      "administrative_expenses         0.005240\n",
      "excess_or_deficit_for_year      0.000520\n",
      "fundraising_expenses            0.000572\n",
      "net_assets                      0.000006\n",
      "other_revenue                   0.002446\n",
      "payments_to_affiliates          0.042524\n",
      "Animals                    -13465.682483\n",
      "Arts, Culture, Humanities   34751.360024\n",
      "Community Development      -20476.501716\n",
      "Education                   13351.010698\n",
      "Environment                -14727.316845\n",
      "Health                      11490.032911\n",
      "Human Services             -31874.542786\n",
      "Human and Civil Rights       4321.197644\n",
      "International               -4715.320260\n",
      "Religion                   -42470.693227\n",
      "Research and Public Policy  63816.456041\n"
     ]
    }
   ],
   "source": [
    "cdf = pd.DataFrame(linreg.coef_, X.columns, columns = ['Coeficient'])\n",
    "print('Intercept: \\n', linreg.intercept_)\n",
    "print(cdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2:  0.2478\n",
      "MAE:  71913.7754\n",
      "MSE:  13486831535.148\n",
      "RMSE: 116132.8185\n"
     ]
    }
   ],
   "source": [
    "print('R^2: ', linreg.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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}