%reload_ext autotime
import pandas as pd
import plotly.io as pio
import plotly.express as px # Plotting
from tqdm import tqdm
tqdm.pandas()
import statsmodels.api as sm
import statsmodels.formula.api as smf
from sklearn.ensemble import RandomForestRegressor
pio.renderers.default = 'notebook'
pd.options.plotting.backend = "plotly"
pd.set_option("display.max_columns", None)
pd.set_option("display.max_colwidth", 100)

def parse(number):
    try:
        return float(number)
    except ValueError:
        return None

time: 1.9 s (started: 2025-11-10 10:48:22 +13:00)

df = pd.read_excel("LLM_results.xlsx")
df = df[(df.timeLeft == "closed") & (df.goal > 0)]
df.sentiment = df.sentiment.astype(str).str.split(" ").str[0].astype(float)
df.truth = df.truth.astype(str).str.split(" ").str[0].astype(float)
df.urgency = df.urgency.astype(str).str.split(" ").str[0].astype(float)
df["Target_completion"] = df.amountRaised / df.goal * 100
df

time: 7.43 s (started: 2025-11-10 10:48:24 +13:00)

df.gender.value_counts().head(20)

gender
Female                   2430
Male                     1779
Other/unknown             539
unknown                     5
indeterminate/unknown       1
Unknown                     1
Male, Female                1
Name: count, dtype: int64

time: 8.74 ms (started: 2025-11-10 10:48:31 +13:00)

print("\n".join(df[df.gender == "Male and Female"].uri.to_list()))

time: 3.74 ms (started: 2025-11-10 10:48:31 +13:00)

df.gender = df.gender.str.lower().where(df.gender.str.lower().isin(['male', 'female']), 'other')
df.gender.value_counts()

gender
female    2430
male      1779
other      547
Name: count, dtype: int64

time: 19.3 ms (started: 2025-11-10 10:48:31 +13:00)

df.ethnicity.value_counts().head(20)

ethnicity
Caucasian           2088
Pacific Islander     483
unknown              444
Pākehā               399
Māori                387
South Asian          220
Asian                146
Pacific               94
Filipino              82
White                 70
African               57
Unknown               55
Other/unknown         35
Hispanic              26
Samoan                24
Black                 14
Fijian                14
Middle Eastern        11
Indian                10
Latino                10
Name: count, dtype: int64

time: 10.6 ms (started: 2025-11-10 10:48:31 +13:00)

df.ethnicity = df.ethnicity.str.extract(r"(.+?)(?= \(|$)")
df.ethnicity.value_counts().head(20)

ethnicity
Caucasian           2088
Pacific Islander     483
unknown              444
Pākehā               399
Māori                387
South Asian          220
Asian                146
Pacific               94
Filipino              82
White                 70
African               57
Unknown               55
Other/unknown         35
Hispanic              26
Samoan                24
Black                 14
Fijian                14
Middle Eastern        11
Indian                10
Latino                10
Name: count, dtype: int64

time: 32.1 ms (started: 2025-11-10 10:48:31 +13:00)

df.attractiveness = df.attractiveness.apply(parse)
df[df.attractiveness >= 90]

time: 78.5 ms (started: 2025-11-10 10:48:31 +13:00)

df.ethnicity = df.ethnicity.replace({
    "European/Caucasian": "Caucasian",
    "European/White": "Caucasian",
    "European/White": "Caucasian",
    "European/New Zealand": "Caucasian",
    "European/New Zealander": "Caucasian",
    "European/New Zealand European": "Caucasian",
    "Māori/Pacific Islander": "Māori",
    "Asian/Pacific Islander": "Pacific Islander",
    "Maori": "Māori"
})
top_20 = df.ethnicity.value_counts().head(20)
display(top_20)
df.ethnicity = df.ethnicity.where(df.ethnicity.isin(top_20.index), "other")

ethnicity
Caucasian           2088
Pacific Islander     483
unknown              444
Pākehā               399
Māori                390
South Asian          220
Asian                146
Pacific               94
Filipino              82
White                 70
African               57
Unknown               55
Other/unknown         35
Hispanic              26
Samoan                24
Black                 14
Fijian                14
Middle Eastern        11
Latino                10
Indian                10
Name: count, dtype: int64

time: 16.1 ms (started: 2025-11-10 10:48:32 +13:00)

df.condition.str.lower().value_counts().head(20)

condition
cancer                    274
breast cancer             216
gender dysphoria          122
bowel cancer               85
stroke                     79
lung cancer                72
multiple sclerosis         72
melanoma                   55
obesity                    46
endometriosis              44
infertility                43
cerebral palsy             39
ovarian cancer             38
kidney failure             37
type 1 diabetes            36
heart attack               33
traumatic brain injury     32
mental health              31
mental health issues       29
spinal cord injury         27
Name: count, dtype: int64

time: 13.3 ms (started: 2025-11-10 10:48:32 +13:00)

df.sentiment.value_counts()

sentiment
65.0    1008
60.0     817
70.0     651
75.0     567
30.0     548
85.0     230
40.0     212
45.0     147
80.0     143
35.0     124
20.0      86
50.0      54
25.0      48
10.0      36
55.0      33
90.0      21
15.0      16
95.0       7
0.0        7
88.0       1
Name: count, dtype: int64

time: 9.38 ms (started: 2025-11-10 10:48:32 +13:00)

df.use.str.lower().value_counts().head(20)

use
medical expenses                                                         1538
medical expenses, travel expenses                                         890
medical expenses, travel expenses, lost wages                             764
medical expenses, lost wages                                              364
medical expenses, experimental therapies                                  320
travel expenses                                                           158
medical expenses, experimental therapies, travel expenses, lost wages     157
travel expenses, medical expenses                                          93
medical expenses, experimental therapies, travel expenses                  59
travel expenses, lost wages                                                37
medical expenses, experimental therapies, lost wages                       27
medical expenses, lost wages, travel expenses                              19
medical expenses, funeral expenses                                         18
lost wages                                                                 12
lost wages, medical expenses                                               11
experimental therapies, medical expenses                                    7
funeral expenses                                                            7
funeral costs                                                               7
travel expenses, accommodation                                              6
travel expenses, medical expenses, lost wages                               5
Name: count, dtype: int64

time: 11.3 ms (started: 2025-11-10 10:48:32 +13:00)

df.age_group = df.age_group.str.extract(r"(.+?)(?= \(|$)")
df.age_group = df.age_group.str.lower().where(df.age_group.str.lower().isin(['0-14', '15-64', '65+']), 'indeterminate/unknown')
df.age_group.value_counts()

age_group
15-64                    2888
65+                       790
0-14                      668
indeterminate/unknown     410
Name: count, dtype: int64

time: 28.9 ms (started: 2025-11-10 10:48:32 +13:00)

df.gender.value_counts()

gender
female    2430
male      1779
other      547
Name: count, dtype: int64

time: 4.1 ms (started: 2025-11-10 10:48:32 +13:00)

df["Charity"] = df.whos_involved.str.contains(r"(Charity)", regex=False)

time: 4.79 ms (started: 2025-11-10 10:48:32 +13:00)

df.to_excel("LLM_results_cleaned.xlsx", index=False)

time: 6.21 s (started: 2025-11-10 10:48:32 +13:00)

df["met_target"] = (df.amountRaised >= df.goal).astype(int)

time: 1.78 ms (started: 2025-11-10 10:48:38 +13:00)

df["parsed_age"] = df.age.apply(parse)
df.parsed_age.value_counts().head(20)

parsed_age
35.0    605
45.0    369
65.0    315
55.0    242
30.0    181
25.0    166
0.0     156
32.0    115
28.0    109
38.0     86
60.0     83
52.0     80
40.0     77
42.0     73
50.0     70
62.0     64
58.0     58
70.0     54
6.0      53
5.0      51
Name: count, dtype: int64

time: 6.84 ms (started: 2025-11-10 10:48:38 +13:00)

df.met_target.value_counts()

met_target
0    4086
1     670
Name: count, dtype: int64

time: 3.37 ms (started: 2025-11-10 10:48:38 +13:00)

df.deservingness = df.deservingness.apply(parse).astype(float)
df.deservingness.describe()

count    4752.000000
mean       89.255892
std         8.391354
min         0.000000
25%        85.000000
50%        90.000000
75%        95.000000
max       100.000000
Name: deservingness, dtype: float64

time: 11 ms (started: 2025-11-10 10:48:38 +13:00)

import pandas as pd
import numpy as np
from scipy.stats import f_oneway, chi2_contingency, pearsonr
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import LabelEncoder

def analyze_feature_influence(df, target_col='met_target'):
    results = {}
    target = df[target_col]
    is_target_numeric = np.issubdtype(target.dtype, np.number)

    for col in df.columns:
        if col == target_col:
            continue
        
        feature = df[col]
        if feature.isnull().any() or target.isnull().any():
            continue  # skip missing for simplicity

        if is_target_numeric:
            # Target is numeric
            if np.issubdtype(feature.dtype, np.number):
                # Pearson correlation
                corr, p_value = pearsonr(feature, target)
                results[col] = {
                    'test': 'pearsonr',
                    'stat': corr,
                    'p_value': p_value
                }
            else:
                # ANOVA (feature is categorical)
                groups = [target[feature == val] for val in feature.unique()]
                stat, p_value = f_oneway(*groups)
                results[col] = {
                    'test': 'anova',
                    'stat': stat,
                    'p_value': p_value
                }
        else:
            # Target is categorical
            if not np.issubdtype(feature.dtype, np.number):
                # Chi-squared test
                contingency = pd.crosstab(df[col], target)
                stat, p, _, _ = chi2_contingency(contingency)
                results[col] = {
                    'test': 'chi2',
                    'stat': stat,
                    'p_value': p
                }

    return pd.DataFrame(results).T.sort_values('p_value')

# Usage:
# df = pd.read_csv('your_data.csv')
df.met_target = df.met_target.astype(bool)
analyze_feature_influence(df, target_col='met_target')

time: 975 ms (started: 2025-11-10 10:48:38 +13:00)

df.smiling = df.smiling.astype(bool)

time: 860 μs (started: 2025-11-10 10:48:39 +13:00)

df["have_age"] = ~df.parsed_age.isna()
df[["met_target", "have_age"]].value_counts()

met_target  have_age
False       True        3830
True        True         625
False       False        256
True        False         45
Name: count, dtype: int64

time: 6.7 ms (started: 2025-11-10 10:48:39 +13:00)

df["log_amountRaised"] = np.log1p(df.amountRaised)
df["log_amountRaised"].describe()

count    4756.000000
mean        7.432131
std         1.893607
min         0.000000
25%         6.216606
50%         7.483525
75%         8.781019
max        13.388103
Name: log_amountRaised, dtype: float64

time: 6.09 ms (started: 2025-11-10 10:48:39 +13:00)

df.start = pd.to_datetime(df.start, format="mixed")
df.end = pd.to_datetime(df.end, format="mixed")
df["days"] = (df.end - df.start).dt.days
df.days.describe()

count    4756.000000
mean      130.953532
std       140.808665
min         0.000000
25%        32.000000
50%        90.000000
75%       182.000000
max      1755.000000
Name: days, dtype: float64

time: 219 ms (started: 2025-11-10 10:48:39 +13:00)

formula = "log_amountRaised ~ days + sentiment + smiling + gender + truth + urgency + parsed_age"
logit = smf.glm(formula=formula, data=df, family=sm.families.Gamma(link=sm.families.links.Log())).fit()
display(logit.summary())
display(f"AIC (Logit): {logit.aic:.2f}")
probit = smf.glm(formula=formula, data=df, family=sm.families.Gamma(link=sm.families.links.Log())).fit()
display(probit.summary())
display(f"AIC (Probit): {probit.aic:.2f}")

/home/ubuntu/givealittle/.venv/lib/python3.12/site-packages/statsmodels/genmod/families/family.py:812: RuntimeWarning:

divide by zero encountered in log

/home/ubuntu/givealittle/.venv/lib/python3.12/site-packages/statsmodels/genmod/generalized_linear_model.py:1891: RuntimeWarning:

invalid value encountered in scalar subtract

'AIC (Logit): -inf'

/home/ubuntu/givealittle/.venv/lib/python3.12/site-packages/statsmodels/genmod/families/family.py:812: RuntimeWarning:

divide by zero encountered in log

/home/ubuntu/givealittle/.venv/lib/python3.12/site-packages/statsmodels/genmod/generalized_linear_model.py:1891: RuntimeWarning:

invalid value encountered in scalar subtract

'AIC (Probit): -inf'

time: 176 ms (started: 2025-11-10 10:48:39 +13:00)

df.met_target = df.met_target.astype(int)
smf.ols(formula="Target_completion ~ days + sentiment + smiling + gender + truth + urgency + parsed_age", data=df).fit().summary()

time: 49.2 ms (started: 2025-11-10 10:48:40 +13:00)

import seaborn as sns
sns.regplot(x="amountRaised", y="smiling", data=df, logistic=True)

<Axes: xlabel='amountRaised', ylabel='smiling'>

time: 7.83 s (started: 2025-11-10 10:48:40 +13:00)

smf.ols(formula=formula, data=df).fit().summary()

time: 90.4 ms (started: 2025-11-10 10:48:47 +13:00)

model = RandomForestRegressor()
cols = "sentiment + smiling + gender + truth + urgency + ethnicity".split(" + ")
X = pd.get_dummies(df[cols])
y = df["met_target"]
model.fit(X, y)

RandomForestRegressor()

time: 797 ms (started: 2025-11-10 10:48:48 +13:00)

list(zip(cols, model.feature_importances_))

[('sentiment', np.float64(0.21714220780964652)),
 ('smiling', np.float64(0.09765563879988882)),
 ('gender', np.float64(0.12161975328506257)),
 ('truth', np.float64(0.1969747348218822)),
 ('urgency', np.float64(0.04706132967039141)),
 ('ethnicity', np.float64(0.04718095039393153))]

time: 10.5 ms (started: 2025-11-10 10:48:48 +13:00)

df.region.value_counts().head(20)

region
Auckland                 1437
Canterbury                587
Wellington                512
Waikato                   413
Bay of Plenty             311
Nationwide                204
Otago                     180
Northland                 167
Hawke's Bay               137
indeterminate/unknown     122
Manuwatū-Whanganui        116
Taranaki                  105
Manawatū-Whanganui         96
Nelson                     90
Southland                  75
International              54
Marlborough                42
Gisborne                   36
West Coast                 25
Tasman                     18
Name: count, dtype: int64

time: 4.66 ms (started: 2025-11-10 10:48:48 +13:00)

	Index	title	pitch	hero	uri	amountRaised	goal	timeLeft	action	actionUri	start	end	n_questions	location	n_donors	description	use_of_funds	whos_involved	moderated	n_updates	updates	_21	text	condition	ICD10	ICD	name	gender	age	age_group	ethnicity	urgency	sentiment	truth	notes	smiling	deservingness	attractiveness	use	region	narrative_clarity	narrative_quality	emotional_tone	image_type	face_visible	facial_expression	image_quality	progression	treatment	treatment_effectiveness	treatment_side_effects	site	stage	reason	narrative_clarity	emotional_tone	facial_expression	progression	treatment	treatment_effectiveness	treatment_side_effects	site	stage	reason	Target_completion
21	21	Need roughly $30.000 for unfunded cancer treatment (Abiraterone Acetate+Methylprednisolone)	Your support would mean the world, helping cover these costs so I can focus on cherishing every ...	https://static.givealittle.co.nz/assets/hero/e7f518e2-a54e-4fa7-9763-b37c00fedf16-320:False	https://givealittle.co.nz/cause/need-dollar30000-for-unfunded-cancer-treatment	265.0	35000.0	closed	Read more	https://givealittle.co.nz/cause/need-dollar30000-for-unfunded-cancer-treatment	20 Oct 2025	24 Oct 2025	0	Nationwide	10.0	In July 2024 I was diagnosed with Metastatic prostate cancer. Metastatic as it has spread extens...	My Cancer treatment (Abiraterone Acetate+Methylprednisolone) which isnt funded in New Zealand	Created by, and paying to a verified bank account of,\r\nBruce Alpine	Page Moderated	NaN	NaN	NaN	Title: Need roughly $30.000 for unfunded cancer treatment (Abiraterone Acetate+Methylprednisolon...	Metastatic prostate cancer	C61	Chapter C00-D49 - Neoplasms	Unknown	Male	65	65+	Caucasian	95.0	65.0	90.0	The person is likely of Caucasian ethnicity based on appearance in the image. The campaign is fo...	0	90.0	70.0	medical expenses	Nationwide	90.0	85.0	desperate	portrait	1	serious	high	95	70	40	60	prostate	IV	The individual is seeking funds to cover the cost of unfunded cancer treatment (Abiraterone Acet...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.757143
87	87	Support Mum’s Urgent Heart Surgery	To raise funds for our mum’s open-heart surgery. Any contribution is deeply appreciated as we st...	https://static.givealittle.co.nz/assets/hero/d4a8d0e0-f9f9-4040-9f7a-b36301448e77-320:False	https://givealittle.co.nz/cause/help-save-our-mums-heart	8520.0	15000.0	closed	Read more	https://givealittle.co.nz/cause/help-save-our-mums-heart	25 Sep 2025	3 Oct 2025	0	Auckland	44.0	On Monday, September 15th, our mum suffered a heart attack. Doctors discovered three major arter...	Procedure and hospital expenses	Created by, and paying to a verified bank account of,\r\nCielo Tionko on behalf of My Mum's...	Page Moderated	NaN	NaN	NaN	Title: Support Mum’s Urgent Heart Surgery\nPitch: To raise funds for our mum’s open-heart surger...	Heart attack	I21	Chapter I00-I99 - Diseases of the circulatory system	Cielo Tionko's mum	Female	65	65+	Asian	95.0	70.0	90.0	The text is clear and heartfelt, with a strong narrative about a heart attack and urgent surgery...	1	95.0	75.0	medical expenses	Auckland	90.0	85.0	desperate	portrait	1	smiling	high	85	0	0	0	NaN	NaN	To raise funds for urgent open-heart triple bypass surgery due to a heart attack with blocked ar...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	56.800000
90	90	Please Help Mum Drive Her Car	Please help mum to modify her car to a left foot accelerator.	https://static.givealittle.co.nz/assets/hero/fd316603-730e-4d7a-8d2a-b3630141de75-320:False	https://givealittle.co.nz/cause/please-help-mum-drive-her-car	3330.0	3500.0	closed	Read more	https://givealittle.co.nz/cause/please-help-mum-drive-her-car	24 Sep 2025	24 Oct 2025	0	Taihape, Manawatu-Wanganui	67.0	"3 and a half years ago, my mum had an emergency right, below the knee amputation to save her li...	All funds raised will be spent on having my car modified to a left foot accelerator.	Created by, and paying to a verified bank account of,\r\nSally Margaret Chambers	Page Moderated	NaN	NaN	NaN	Title: Please Help Mum Drive Her Car\nPitch: Please help mum to modify her car to a left foot ac...	Below-knee amputation	T87.8	Chapter S00-T88 - Injury, poisoning and certain other consequences of external causes	Sally Margaret Chambers	Female	65	65+	Pākehā	85.0	60.0	85.0	The text appears to be a genuine appeal for help with a car modification. The narrative is clear...	0	90.0	70.0	medical expenses, travel expenses	Manawatū-Whanganui	90.0	80.0	desperate	portrait	1	neutral	medium	70	80	60	75	NaN	NaN	The funds will be used to modify the car with a left foot accelerator so that the mother, who ha...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	95.142857
92	92	A mother's love, a daughters fight - Help us heal a Liver and Life	A mother's love, a daughters fight - Help us heal a Liver and Life	https://static.givealittle.co.nz/assets/hero/acf99948-53f5-4846-a2d7-b362011ba176-320:False	https://givealittle.co.nz/cause/a-mothers-love-a-daughters-fight-help-us-heal	1550.0	1500.0	closed	Read more	https://givealittle.co.nz/cause/a-mothers-love-a-daughters-fight-help-us-heal	24 Sep 2025	8 Oct 2025	0	Bay of Plenty	36.0	After a suicide attempt, Kayla age 13 is in hospital with severe liver failure and needs urgent ...	Ambulance fees, Loss of income, travel and accommodation for family, prescription medications, r...	Created by\r\nHayley George \n\n\n\n\r\n Paying to a verified bank account of\r\nKrystle G...	Page Moderated	3.0	Update 01/10/2025\n1 October 2025\nKayla continues to fight bravely on her recovery journey. Her...	NaN	Title: A mother's love, a daughters fight - Help us heal a Liver and Life\nPitch: A mother's lov...	Liver failure	K76.8	Chapter K00-K95 - Diseases of the digestive system	Kayla	Female	13	0-14	Pacific Islander	90.0	65.0	85.0	The image shows a young girl in a hospital bed, consistent with the narrative of a 13-year-old p...	0	95.0	70.0	medical expenses, travel expenses, lost wages	Bay of Plenty	90.0	85.0	hopeful	portrait	1	neutral	medium	70	60	50	60	NaN	NaN	Kayla, a 13-year-old girl, is in hospital with severe liver failure following a suicide attempt....	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	103.333333
93	93	Please make Linetas Journey with Cancer less stressful by donating to her good cause to prolong ...	Requesting any donations to help with Lineta Uelese's Cancer Treatments. 6x Chemotherapy needed....	https://static.givealittle.co.nz/assets/hero/a34be6ac-331d-4fc1-ad60-b36200c72db3-320:False	https://givealittle.co.nz/cause/please-make-linetas-journey-with-cancer-less	1510.0	70000.0	closed	Read more	https://givealittle.co.nz/cause/please-make-linetas-journey-with-cancer-less	24 Sep 2025	24 Oct 2025	0	Manurewa, Auckland	14.0	Please help suppport my sister-in-law Lineta Uelese. She is\na 39-year-old mother from Samoa.\nS...	Funds will go towards 6 Chemotherapy treatments plus other costs for her medical bills.	Created by, and paying to a verified bank account of,\r\nFiti Tapelu on behalf of Lineta Ue...	Page Moderated	NaN	NaN	NaN	Title: Please make Linetas Journey with Cancer less stressful by donating to her good cause to p...	Ovarian Cancer	C56.9	Chapter C00-D49 - Neoplasms	Lineta Uelese	Female	39	15-64	Samoan	95.0	65.0	90.0	Ethnicity inferred from text mentioning 'mother from Samoa'. Image shows a person with short hai...	1	95.0	70.0	medical expenses, experimental therapies, travel expenses, lost wages	Auckland	85.0	75.0	desperate	selfie	1	smiling	high	75	50	40	60	Ovary	3	Lineta Uelese, a 39-year-old mother of six from Samoa, is undergoing treatment for Stage 3 Ovari...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2.157143
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
11725	11733	New Zealand MindBody Trust web-based 'whole person' resources for healthworkers and their patien...	The NZ MindBody Trust web-based project to support and educate clinicians and patients in a 'who...	https://static.givealittle.co.nz/assets/hero/158d0e80-2c47-449a-a05e-a603014e786e-320:False	https://givealittle.co.nz/cause/mindbodyresourcesforpatientsandhealthworkers	6700.0	9000.0	closed	Read more	https://givealittle.co.nz/cause/mindbodyresourcesforpatientsandhealthworkers	9 May 2016	15 Jul 2016	0	Auckland	36.0	The NZ MindBody Trust (2004) is concerned with 'whole person' and person-centred approaches to h...	NaN	Created by, and paying to a verified bank account of,_x000D_\n _x000D_\n New Zealand...	Page Moderated	2.0	The Give-A-Little Campaign Result\n12 July 2016\nThe MindBody Trustees want to thank you all for...	NaN	Title: New Zealand MindBody Trust web-based 'whole person' resources for healthworkers and their...	Chronic disorders	Z99.8	Chapter Z00-Z99 - Factors influencing health status and contact with health services	Brian Broom	Male	55	15-64	Caucasian	50.0	65.0	85.0	The image is a logo, not a person. The person is not visible, so all person-related attributes a...	0	70.0	50.0	medical expenses	Auckland	85.0	75.0	NaN	symbolic	0	not_detectable	high	50	50	50	50	NaN	NaN	To fund the development of a free, accessible website providing 'whole person' health resources ...	NaN	grateful	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	74.444444
11727	11735	Diane's cyst removal to continue my dream job	Large Radial Ganglion Cyst Removal	https://static.givealittle.co.nz/assets/hero/06af4a19-e239-44bb-a5a8-a60400ab4982-320:False	https://givealittle.co.nz/cause/wristoperation	730.0	5000.0	closed	Read more	https://givealittle.co.nz/cause/wristoperation	9 May 2016	3 Jun 2016	0	Northland	11.0	I am in need of a large ganglion cyst removed from my left wrist. It has entwined itself with 2 ...	NaN	Created by, and paying to a verified bank account of,_x000D_\n _x000D_\n Diane Crocker	Page Moderated	NaN	NaN	NaN	Title: Diane's cyst removal to continue my dream job\nPitch: Large Radial Ganglion Cyst Removal...	Radial Ganglion Cyst	M14.0	Chapter M00-M99 - Diseases of the musculoskeletal system and connective tissue	Diane	Female	55	15-64	Caucasian	75.0	50.0	90.0	Image suggests a middle-aged woman, likely Caucasian. Text is factual and straightforward. Campa...	0	85.0	60.0	medical expenses	Northland	85.0	75.0	desperate	portrait	1	neutral	medium	60	0	0	0	NaN	NaN	Diane needs a large radial ganglion cyst removed from her left wrist, which is causing pain, los...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	14.600000
11735	11743	A thank you to Waikato NICU for helping so many little babies and their families	Raising funds for Waikato NICU, and a portable cardiac monitor to support the miracle babies.	https://static.givealittle.co.nz/assets/hero/75b48fa1-0752-44b4-8484-a5fb015ecab2-320:False	https://givealittle.co.nz/cause/charlieandoliver	8912.7	35000.0	closed	Read more	https://givealittle.co.nz/cause/charlieandoliver	3 May 2016	30 Jun 2017	0	Waikato	100.0	This give-a-little page aims to raise funds to allow the NICU at Waikato Hospital to purchase a ...	NaN	Created by_x000D_\n _x000D_\n Rebecca Clews_x000D_\n \n\n\n\n\n\n\n_x000D_\n ...	Page Moderated	4.0	Quiz Night!\n7 November 2016\nGot no plans on the 17th of November? Come on down to the Speights...	NaN	Title: A thank you to Waikato NICU for helping so many little babies and their families\nPitch: ...	Prematurity	P07.9	Chapter P00-P96 - Certain conditions originating in the perinatal period	Rebecca Clews	Female	31	15-64	Caucasian	85.0	65.0	95.0	The person in the image is a woman holding a premature baby. The text states that the twins were...	0	80.0	60.0	medical expenses, travel expenses	Waikato	85.0	75.0	grateful	portrait	1	neutral	medium	50	70	80	30	NaN	NaN	The campaign is raising funds for a portable cardiac monitor for the NICU at Waikato Hospital. T...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	25.464857
11736	11744	Help restore David’s smile after being assaulted	Talented professional musician and entertainer David Shanhun needs your help to restore his teet...	https://static.givealittle.co.nz/assets/hero/ee0d17f1-b6a5-4d41-85e9-a5fa0166b741-320:False	https://givealittle.co.nz/cause/help-restore-davids-teeth-and-smile	8048.0	54500.0	closed	Read more	https://givealittle.co.nz/cause/help-restore-davids-teeth-and-smile	3 May 2016	31 Jul 2016	0	Auckland	115.0	David was assaulted on the 16th of April 2016. He had 2 teeth knocked out completely, and 10 tee...	NaN	Created by_x000D_\n _x000D_\n Fiona Shanhun_x000D_\n \n\n\n\n\n\n\n_x000D_\n ...	Page Moderated	2.0	Health/Teeth/Face Update\n24 May 2016\n*Message from David:*\nThank you all so much for your...	NaN	Title: Help restore David’s smile after being assaulted\nPitch: Talented professional musician a...	dental trauma	S00.8	Chapter S00-T88 - Injury, poisoning and certain other consequences of external causes	David Shanhun	Male	32	15-64	Caucasian	75.0	85.0	90.0	Image suggests a Caucasian male. Age estimated based on appearance. Campaign narrative is clear ...	1	95.0	80.0	medical expenses, lost wages	Auckland	90.0	85.0	hopeful	portrait	1	smiling	high	60	40	30	50	NaN	NaN	David needs $26,500 for immediate dental treatment including root canals and implants, and over ...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	14.766972
11745	11753	TRUS Biopsy Machine for Taranaki	The men of Taranaki need this TRUS Biopsy Machine to diagnose Prostate Cancer. Reduce waiting t...	https://static.givealittle.co.nz/assets/hero/c9169845-63b5-4c1e-bb50-a5fc00e5abd0-320:False	https://givealittle.co.nz/cause/prostatebiopsymachinenewplymouth	1910.0	20000.0	closed	Read more	https://givealittle.co.nz/cause/prostatebiopsymachinenewplymouth	29 Apr 2016	31 Aug 2016	0	Taranaki	15.0	Currently the TRUS (Trans Rectal Ultrasound) machine for diagnosing prostate cancer is only avai...	NaN	Created by, and paying to a verified bank account of,_x000D_\n _x000D_\n Rotary Club...	Page Moderated	NaN	NaN	NaN	Title: TRUS Biopsy Machine for Taranaki\nPitch: The men of Taranaki need this TRUS Biopsy Machin...	Prostate Cancer	C61	Chapter C00-D49	Men of Taranaki	Male	indeterminate/unknown	indeterminate/unknown	unknown	70.0	60.0	85.0	The image is symbolic, not a portrait of a specific individual. The person's ethnicity is not me...	0	75.0	50.0	medical expenses	Taranaki	85.0	75.0	hopeful	symbolic	0	not_detectable	high	50	0	0	0	prostate	indeterminate/unknown	To purchase a TRUS Biopsy Machine for early diagnosis of prostate cancer, reducing waiting times...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	9.550000

	Index	title	pitch	hero	uri	amountRaised	goal	timeLeft	action	actionUri	start	end	n_questions	location	n_donors	description	use_of_funds	whos_involved	moderated	n_updates	updates	_21	text	condition	ICD10	ICD	name	gender	age	age_group	ethnicity	urgency	sentiment	truth	notes	smiling	deservingness	attractiveness	use	region	narrative_clarity	narrative_quality	emotional_tone	image_type	face_visible	facial_expression	image_quality	progression	treatment	treatment_effectiveness	treatment_side_effects	site	stage	reason	narrative_clarity	emotional_tone	facial_expression	progression	treatment	treatment_effectiveness	treatment_side_effects	site	stage	reason	Target_completion
220	220	Life Saving Surgery For Esteban Our Mini Dachshund (IVDD)	Fund raising to get Esteban the mini dachshund life saving surgery.	https://static.givealittle.co.nz/assets/hero/0f9946bb-6956-497d-a8e9-b34401554923-320:False	https://givealittle.co.nz/cause/life-saving-surgery-for-esteban-our-mini-dachshund	5485.00	5000.0	closed	Read more	https://givealittle.co.nz/cause/life-saving-surgery-for-esteban-our-mini-dachshund	25 Aug 2025	8 Sep 2025	0	Dunedin, Otago	105.0	Please Help Us Save Esteban’s Life\nEsteban has been diagnosed with Intervertebral Disc Disease,...	Surgery and aftercare costs for Esteban.	Created by, and paying to a verified bank account of,\r\nSam Heath	Page Moderated	NaN	NaN	NaN	Title: Life Saving Surgery For Esteban Our Mini Dachshund (IVDD)\nPitch: Fund raising to get Est...	Intervertebral Disc Disease	M51.2	Chapter M00-M99 - Diseases of the musculoskeletal system and connective tissue	Esteban	other	5	0-14	unknown	95.0	65.0	90.0	The image shows a dachshund, not a human, so all human attributes are inferred from the text. Th...	0	85.0	90.0	medical expenses	Otago	90.0	85.0	desperate	portrait	1	neutral	high	70	0	0	0	NaN	NaN	Donated funds will be used to cover the cost of life-saving surgery, aftercare, and rehabilitati...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	109.700000
441	441	Tragic family circumstances	Tragic family circumstances	https://static.givealittle.co.nz/assets/hero/47874955-526a-40bd-9682-b31300cb2790-320:False	https://givealittle.co.nz/cause/tragic-family-circumstances	2190.00	5000.0	closed	Read more	https://givealittle.co.nz/cause/tragic-family-circumstances	7 Jul 2025	28 Jul 2025	0	Marlborough	39.0	Five days ago Valley was 5 months old, today she is on life support in Canada waiting for her gr...	Return flights to Canada/NZ, food	Created by\r\nRa Davis \n\n\n\n\r\n Paying to a verified bank account of\r\nDebbie Huia	Page Moderated	1.0	Tragic family circumstances\n15 July 2025\nAs many of you are aware Debz moko passed away in her...	NaN	Title: Tragic family circumstances\nPitch: Tragic family circumstances\nDescription: Five days a...	Neonatal death	P96.9	Chapter P00-P96 - Certain conditions originating in the perinatal period	Valley	female	5	0-14	Māori	95.0	10.0	85.0	The text mentions a tragic event involving a 5-month-old baby. The image shows a smiling baby, w...	1	90.0	95.0	travel expenses, medical expenses	Marlborough	85.0	70.0	desperate	portrait	1	smiling	high	100	0	0	0	NaN	NaN	Funds are needed for Deb to travel to Canada to retrieve her deceased granddaughter's body and t...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	43.800000
1085	1085	A Helmet for Amelia’s Bright Future	Helping baby Amelia get the orthotic helmet she needs — because every little head deserves a hea...	https://static.givealittle.co.nz/assets/hero/1767aff8-a54d-40d8-9566-b2c7017108a3-320:False	https://givealittle.co.nz/cause/a-helmet-for-amelias-bright-future	2501.00	4200.0	closed	Read more	https://givealittle.co.nz/cause/a-helmet-for-amelias-bright-future	22 Apr 2025	13 May 2025	0	Hamilton, Waikato	75.0	My bright baby girl, Amelia, has been diagnosed with cranial deformities. The recommended treatm...	Orthotic cranial helmet and therapy for Amelia at Orthotic House, NZ (https://www.orthotichouse....	Created by, and paying to a verified bank account of,\r\n \r\n Dil Askarova\r\n ...	Page Moderated	NaN	NaN	NaN	Title: A Helmet for Amelia’s Bright Future\nPitch: Helping baby Amelia get the orthotic helmet s...	Cranial deformities	Q75.0	Chapter Q00-Q99 - Congenital malformations, deformations and chromosomal abnormalities	Amelia	female	6	0-14	Caucasian	85.0	80.0	90.0	The text is consistent and clear, with no obvious signs of fabrication. The image shows a baby g...	1	95.0	90.0	medical expenses	Waikato	95.0	85.0	hopeful	portrait	1	smiling	high	30	0	0	0	NaN	NaN	Donated funds will be used to cover the cost of a custom cranial remodeling helmet and follow-up...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	59.547619
2284	2284	Remi Rocks	Help us get Remi to Brisbane for intensive physical therapy	https://static.givealittle.co.nz/assets/hero/df7bb718-0d4a-422b-af53-b179014ca04a-320:False	https://givealittle.co.nz/cause/remi-rocks	24675.00	30000.0	closed	Read more	https://givealittle.co.nz/cause/remi-rocks	24 May 2024	30 Jun 2024	0	Mount Maunganui, Tauranga	251.0	Remi's story is still unwritten. After almost 2 years of tests and investigations we still don't...	Any money raised will go towards the cost of the intensive therapy as well as flights and accomm...	Created by, and paying to a verified bank account of,_x000D_\n _x000D_\n Mandy Hende...	Page Moderated	1.0	Let’s go again!\n30 May 2024\nBecause you guys are legends we are raising the goal. Anything ove...	NaN	Title: Remi Rocks\nPitch: Help us get Remi to Brisbane for intensive physical therapy\nDescripti...	neurodevelopmental disorder	R68.8	Chapter R00-R99 - Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere c...	Remi	female	3	0-14	Caucasian	85.0	70.0	80.0	The campaign is for a child with unexplained neurodevelopmental challenges. The text is emotiona...	1	95.0	90.0	medical expenses, travel expenses	Bay of Plenty	90.0	85.0	hopeful	portrait	1	smiling	high	50	60	40	20	NaN	NaN	Donated funds will be used to cover the cost of intensive paediatric rehabilitation therapy in B...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	82.250000
3346	3348	Please help our family get Alayna to Aussie for Intensive Therapy.	We are asking for help for our youngest daughter Alayna to get the intensive therapies she needs...	https://static.givealittle.co.nz/assets/hero/3cf9aae0-dbb5-43fe-93d9-aff501102de8-320:False	https://givealittle.co.nz/cause/please-help-our-family-get-to-aussie	14960.00	14000.0	closed	Read more	https://givealittle.co.nz/cause/please-help-our-family-get-to-aussie	1 May 2023	1 Jun 2023	1	Auckland	126.0	Our littlest Lever has a rare genetic condition called CACNA1A which comes with life altering ch...	All funds raised will be put towards flights and accommodation for our family, 2 weeks of intens...	Created by, and paying to a verified bank account of,_x000D_\nNicole Lever	Page Moderated	1.0	We made it!\n12 June 2023\nWe have made it to the Gold Coast and have completed our first day of...	on 7 May 2023\ndonna asks\nHave you given yourself a buffer for extra costs?\nNicole\nI thought ...	Title: Please help our family get Alayna to Aussie for Intensive Therapy.\nPitch: We are asking ...	CACNA1A	G11.8	Chapter G00-G99 - Diseases of the nervous system	Alayna	female	2	0-14	Pākehā	85.0	70.0	80.0	The text is clear and well-written. The campaign is for a young child with a rare genetic condit...	1	95.0	90.0	medical expenses, travel expenses	Auckland	90.0	85.0	hopeful	portrait	1	smiling	high	40	30	40	20	NaN	NaN	To fund a 2-week intensive therapy program at The Centre of Movement in Australia for Alayna, wh...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	106.857143
3697	3699	Help Hudson get his hearing and speech sorted.	Help Hudson get the surgery he needs for his speech and overall health after battling the public...	https://static.givealittle.co.nz/assets/hero/beb66e05-44a7-4e0b-b710-af8e01417ec9-320:False	https://givealittle.co.nz/cause/help-hudson-get-his-hearing-sorted	121.00	9000.0	closed	Read more	https://givealittle.co.nz/cause/help-hudson-get-his-hearing-sorted	18 Jan 2023	18 Feb 2023	0	Farm Cove, Auckland	3.0	Since Hudson was 1 1/2 as parents we have known there was something wrong with his speech. We ha...	The money will be used to get Hudson’s surgery done privately but if not enough will be used to ...	Created by, and paying to a verified bank account of,_x000D_\nChloe Milner	Page Moderated	NaN	NaN	NaN	Title: Help Hudson get his hearing and speech sorted.\nPitch: Help Hudson get the surgery he nee...	Sleep apnoea, hearing impairment, speech delay	H61.2, R06.0, R48.8, R49.8, R49.9, R65.9, R67.8, R67.9, R93.8, R93.9, R94.8, R94.9, R95.8, R95.9...	Chapter H60-H95 - Diseases of the ear and mastoid process	Hudson	male	3	0-14	Pacific Islander	90.0	30.0	85.0	The image shows a young boy with a big smile. The narrative is clear and well-structured. The pe...	1	95.0	90.0	medical expenses, experimental therapies, travel expenses, lost wages	Auckland	90.0	85.0	desperate	portrait	1	smiling	high	60	40	30	50	NaN	NaN	Hudson needs urgent surgery to address his sleep apnoea and hearing issues, which are affecting ...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.344444
5141	5145	Baby Atlas	Helping Baby Atlas	https://static.givealittle.co.nz/assets/hero/09faf6e8-55ca-4028-b0b9-adf500c40bdd-320:False	https://givealittle.co.nz/cause/baby-atlas	5744.77	5000.0	closed	Read more	https://givealittle.co.nz/cause/baby-atlas	5 Dec 2021	5 Jun 2022	0	Auckland	68.0	I'm setting up this page to hopefully raise funds on behalf of my bestfriend Hayley and her fian...	Oxygen/breathing monitor, everyday household bills.	Created by_x000D_\nAmberose Carson \n\n\n\n_x000D_\n Paying to a verified bank account of_...	Page Moderated	NaN	NaN	NaN	Title: Baby Atlas\nPitch: Helping Baby Atlas\nDescription: I'm setting up this page to hopefully...	Neurofibromatosis type 1	Q85.0	Chapter Q00-Q99 - Congenital malformations, deformations and chromosomal abnormalities	Atlas	male	2	0-14	Caucasian	90.0	30.0	95.0	The text is clear and the narrative is consistent. The image shows a baby who appears healthy, w...	1	95.0	90.0	medical expenses, lost wages	Auckland	85.0	70.0	desperate	portrait	1	smiling	high	60	40	30	50	NaN	NaN	Funds are needed for an oxygen/breathing monitor for Atlas when he returns home and to help with...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	114.895400
8099	8104	Help baby Miné to hear	Help baby Miné to receive cochlear implants	https://static.givealittle.co.nz/assets/hero/aadffa91-73dc-48ed-bb37-aa5101575a16-320:False	https://givealittle.co.nz/cause/help-baby-mine-to-hear	435.00	55000.0	closed	Read more	https://givealittle.co.nz/cause/help-baby-mine-to-hear	18 May 2019	1 Nov 2019	0	Bay of Plenty	8.0	We, New Zealand citizens for more than 15 years, have a granddaughter, Miné, who was born in Jan...	Funding of cochlear implant operation, devices and after-care for baby Miné. Surplus will be use...	Created by, and paying to a verified bank account of,_x000D_\nHannes & Charille Schoeman on...	Page Moderated	1.0	2 weeks to go!!\n25 September 2019\nOnly two weeks to go before beautiful little Miné gets her b...	NaN	Title: Help baby Miné to hear\nPitch: Help baby Miné to receive cochlear implants \nDescription:...	congenital deafness	H90.3	Chapter H60-H95 - Diseases of the ear and mastoid process	Miné	female	1	0-14	White	95.0	70.0	90.0	The image shows a baby girl with light skin and blue eyes, suggesting a White ethnicity. The nar...	1	95.0	90.0	medical expenses, experimental therapies	Bay of Plenty	90.0	85.0	desperate	portrait	1	smiling	high	50	0	0	0	NaN	NaN	Donated funds will be used to cover the cost of the second cochlear implant and associated medic...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.790909

Dep. Variable:	log_amountRaised	No. Observations:	4455
Model:	GLM	Df Residuals:	4446
Model Family:	Gamma	Df Model:	8
Link Function:	Log	Scale:	0.056312
Method:	IRLS	Log-Likelihood:	inf
Date:	Mon, 10 Nov 2025	Deviance:	430.25
Time:	10:48:39	Pearson chi2:	250.
No. Iterations:	25	Pseudo R-squ. (CS):	nan
Covariance Type:	nonrobust

	coef	std err	z	P>\|z\|	[0.025	0.975]
Intercept	1.2417	0.056	22.145	0.000	1.132	1.352
smiling[T.True]	0.0826	0.007	11.099	0.000	0.068	0.097
gender[T.male]	0.0305	0.008	4.037	0.000	0.016	0.045
gender[T.other]	-0.0377	0.015	-2.432	0.015	-0.068	-0.007
days	0.0001	2.57e-05	5.821	0.000	9.92e-05	0.000
sentiment	0.0021	0.000	9.875	0.000	0.002	0.003
truth	0.0020	0.001	2.961	0.003	0.001	0.003
urgency	0.0049	0.000	17.627	0.000	0.004	0.005
parsed_age	-0.0002	0.000	-1.242	0.214	-0.001	0.000

Dep. Variable:	log_amountRaised	No. Observations:	4455
Model:	GLM	Df Residuals:	4446
Model Family:	Gamma	Df Model:	8
Link Function:	Log	Scale:	0.056312
Method:	IRLS	Log-Likelihood:	inf
Date:	Mon, 10 Nov 2025	Deviance:	430.25
Time:	10:48:40	Pearson chi2:	250.
No. Iterations:	25	Pseudo R-squ. (CS):	nan
Covariance Type:	nonrobust

	test	stat	p_value
age_group	chi2	41.515522	0.0
ethnicity	chi2	54.962474	0.000042
end	chi2	2309.985027	0.169615
gender	chi2	2.730386	0.255331
start	chi2	2540.523648	0.325432
whos_involved	chi2	4689.48702	0.417515
pitch	chi2	4747.737518	0.490157
hero	chi2	4756.0	0.493182
uri	chi2	4756.0	0.493182
actionUri	chi2	4756.0	0.493182
notes	chi2	4756.0	0.493182
text	chi2	4756.0	0.493182
title	chi2	4728.458393	0.495385
Charity	chi2	0.446239	0.504126
action	chi2	0.0	1.0
timeLeft	chi2	0.0	1.0
moderated	chi2	0.0	1.0

Dep. Variable:	Target_completion	R-squared:	0.002
Model:	OLS	Adj. R-squared:	-0.000
Method:	Least Squares	F-statistic:	0.8567
Date:	Mon, 10 Nov 2025	Prob (F-statistic):	0.553
Time:	10:48:40	Log-Likelihood:	-35922.
No. Observations:	4455	AIC:	7.186e+04
Df Residuals:	4446	BIC:	7.192e+04
Df Model:	8
Covariance Type:	nonrobust

	coef	std err	t	P>\|t\|	[0.025	0.975]
Intercept	-143.5134	181.748	-0.790	0.430	-499.831	212.804
smiling[T.True]	9.0121	24.118	0.374	0.709	-38.271	56.295
gender[T.male]	38.0294	24.477	1.554	0.120	-9.958	86.017
gender[T.other]	35.0388	50.187	0.698	0.485	-63.352	133.430
days	-0.1019	0.083	-1.223	0.221	-0.265	0.061
sentiment	0.1799	0.696	0.258	0.796	-1.185	1.545
truth	2.2916	2.160	1.061	0.289	-1.942	6.526
urgency	0.1624	0.901	0.180	0.857	-1.603	1.928
parsed_age	-0.7003	0.623	-1.125	0.261	-1.921	0.521

Omnibus:	13712.438	Durbin-Watson:	2.004
Prob(Omnibus):	0.000	Jarque-Bera (JB):	1129656429.335
Skew:	46.308	Prob(JB):	0.00
Kurtosis:	2468.180	Cond. No.	3.47e+03

Omnibus:	83.437	Durbin-Watson:	1.996
Prob(Omnibus):	0.000	Jarque-Bera (JB):	99.082
Skew:	-0.275	Prob(JB):	3.05e-22
Kurtosis:	3.480	Cond. No.	3.47e+03

	n_estimators	100
	criterion	'squared_error'
	max_depth	None
	min_samples_split	2
	min_samples_leaf	1
	min_weight_fraction_leaf	0.0
	max_features	1.0
	max_leaf_nodes	None
	min_impurity_decrease	0.0
	bootstrap	True
	oob_score	False
	n_jobs	None
	random_state	None
	verbose	0
	warm_start	False
	ccp_alpha	0.0
	max_samples	None
	monotonic_cst	None

	coef	std err	t	P>\|t\|	[0.025	0.975]
Intercept	1.0209	0.408	2.504	0.012	0.222	1.820
smiling[T.True]	0.6074	0.054	11.227	0.000	0.501	0.714
gender[T.male]	0.2096	0.055	3.816	0.000	0.102	0.317
gender[T.other]	-0.2730	0.113	-2.425	0.015	-0.494	-0.052
days	0.0012	0.000	6.179	0.000	0.001	0.002
sentiment	0.0170	0.002	10.887	0.000	0.014	0.020
truth	0.0207	0.005	4.268	0.000	0.011	0.030
urgency	0.0384	0.002	18.980	0.000	0.034	0.042
parsed_age	-0.0014	0.001	-1.016	0.309	-0.004	0.001