import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('/work/tord_v3_edited.csv')
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6415 entries, 0 to 6414
Data columns (total 34 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 id 6415 non-null int64
1 name 6415 non-null object
2 token 6282 non-null object
3 country 6415 non-null object
4 is_ico 6415 non-null int64
5 is_ieo 6415 non-null int64
6 is_sto 6415 non-null int64
7 ico_start 5636 non-null object
8 ico_end 5497 non-null object
9 price_usd 5928 non-null object
10 raised_usd 2139 non-null float64
11 distributed_in_ico 4870 non-null float64
12 sold_tokens 192 non-null float64
13 token_for_sale 5122 non-null float64
14 whitelist 3882 non-null object
15 kyc 6415 non-null int64
16 bonus 6415 non-null int64
17 restricted_areas 2343 non-null object
18 min_investment 2079 non-null object
19 bounty 6415 non-null int64
20 mvp 1296 non-null object
21 pre_ico_start 2717 non-null object
22 pre_ico_end 2705 non-null object
23 pre_ico_price_usd 1733 non-null object
24 platform 6415 non-null int64
25 accepting 5545 non-null object
26 link_white_paper 5828 non-null object
27 linkedin_link 4355 non-null object
28 github_link 5649 non-null object
29 website 5649 non-null object
30 rating 5709 non-null float64
31 teamsize 4622 non-null float64
32 Coinmarketcap_identifier 1281 non-null float64
33 ERC20 5679 non-null float64
dtypes: float64(8), int64(8), object(18)
memory usage: 1.7+ MB
df.head(20)
df.describe()
# sanitize "raised_usd"
df['raised_usd'] = df['raised_usd'].fillna(0)
# create outcome variable success
df['success'] = np.where(df['raised_usd'] >= 500000, 1, 0)
df['success'].describe()
sns.pairplot(df, vars = ['raised_usd','teamsize','success'])
# Potential multicollinearity issue
corr = df.corr()
sns.heatmap(corr)
plt.show()
!pip install statsmodels
Collecting statsmodels
Downloading statsmodels-0.13.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.8 MB)
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Installing collected packages: patsy, statsmodels
Successfully installed patsy-0.5.2 statsmodels-0.13.0
WARNING: You are using pip version 21.2.4; however, version 21.3 is available.
You should consider upgrading via the '/root/venv/bin/python -m pip install --upgrade pip' command.
import statsmodels.api as sm
from statsmodels.formula.api import ols, logit
# dependent/target/outcome variable
y = df['success']
# independent/predictor/explanatory variable
X = df[['teamsize', 'rating']]
# A. Logit regression
# turn independent variables into floating type (best practice)
# 'missing='drop'' drops rows with missing values from the regression
logit_model=sm.Logit(y,X.astype(float), missing='drop' )
# fit logit model into the data
result=logit_model.fit()
# summarize the logit model
print(result.summary2())
Optimization terminated successfully.
Current function value: 0.633922
Iterations 5
Results: Logit
================================================================
Model: Logit Pseudo R-squared: -0.014
Dependent Variable: success AIC: 5270.6235
Date: 2021-10-22 02:29 BIC: 5283.2872
No. Observations: 4154 Log-Likelihood: -2633.3
Df Model: 1 LL-Null: -2596.2
Df Residuals: 4152 LLR p-value: 1.0000
Converged: 1.0000 Scale: 1.0000
No. Iterations: 5.0000
-----------------------------------------------------------------
Coef. Std.Err. z P>|z| [0.025 0.975]
-----------------------------------------------------------------
teamsize 0.0439 0.0049 8.8867 0.0000 0.0342 0.0536
rating -0.3575 0.0207 -17.2578 0.0000 -0.3981 -0.3169
================================================================
sns.regplot(x = "rating", y = "success", data = df,
logistic = True, y_jitter = .05)
plt.ylabel("success probability")
'''
options for "at"
1. 'overall' The average of the marginal effects at each observation
2. 'mean' The marginal effects at the mean of each regressor
3. 'median' The marginal effects at the median of each regressor
4. 'zero' The marginal effects at zero for each regressor
5. 'all' The marginal effects at each observation.
options for "method"
1. 'dydx' No transformation is made and amrginal effects are returned
2. 'eyex' estimate elasticities of variables in exog
3. 'dyex' estimate semi-elasticity
4. 'eydx' estimate semi-elasticity
'''
average_marginal_effect = result.get_margeff(at = "mean", method = "dydx")
print(average_marginal_effect.summary())
Logit Marginal Effects
=====================================
Dep. Variable: success
Method: dydx
At: mean
==============================================================================
dy/dx std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
teamsize 0.0099 0.001 8.917 0.000 0.008 0.012
rating -0.0806 0.004 -18.929 0.000 -0.089 -0.072
==============================================================================
# B. Linear Probability Model
# logit regression
X = sm.add_constant(X)
ols_model=sm.OLS(y,X.astype(float), missing='drop')
result=ols_model.fit()
print(result.summary2())
Results: Ordinary least squares
==================================================================
Model: OLS Adj. R-squared: 0.071
Dependent Variable: success AIC: 5136.3362
Date: 2021-10-22 02:30 BIC: 5155.3316
No. Observations: 4154 Log-Likelihood: -2565.2
Df Model: 2 F-statistic: 158.6
Df Residuals: 4151 Prob (F-statistic): 4.42e-67
R-squared: 0.071 Scale: 0.20147
--------------------------------------------------------------------
Coef. Std.Err. t P>|t| [0.025 0.975]
--------------------------------------------------------------------
const -0.1899 0.0340 -5.5805 0.0000 -0.2566 -0.1232
teamsize 0.0083 0.0010 7.8945 0.0000 0.0062 0.0103
rating 0.1316 0.0114 11.5235 0.0000 0.1092 0.1540
------------------------------------------------------------------
Omnibus: 3397.013 Durbin-Watson: 1.666
Prob(Omnibus): 0.000 Jarque-Bera (JB): 581.363
Skew: 0.698 Prob(JB): 0.000
Kurtosis: 1.814 Condition No.: 69
==================================================================
# Use wrapper lazypredict
!pip install lazypredict
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Building wheels for collected packages: PyYAML
Building wheel for PyYAML (setup.py) ... done
Created wheel for PyYAML: filename=PyYAML-5.3.1-cp37-cp37m-linux_x86_64.whl size=44635 sha256=5a61ee4b0ef8a8015ad78c04addec84be363468ccbd120c5bc732292ba8cdc33
Stored in directory: /root/.cache/pip/wheels/5e/03/1e/e1e954795d6f35dfc7b637fe2277bff021303bd9570ecea653
Successfully built PyYAML
Installing collected packages: numpy, six, scipy, joblib, scikit-learn, pluggy, more-itertools, xgboost, tqdm, PyYAML, pytest, pandas, lightgbm, click, lazypredict
Attempting uninstall: numpy
Found existing installation: numpy 1.19.5
Not uninstalling numpy at /shared-libs/python3.7/py/lib/python3.7/site-packages, outside environment /root/venv
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Attempting uninstall: pluggy
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Attempting uninstall: tqdm
Found existing installation: tqdm 4.62.3
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Attempting uninstall: PyYAML
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Attempting uninstall: click
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ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
tensorflow 2.4.1 requires numpy~=1.19.2, but you have numpy 1.19.1 which is incompatible.
tensorflow 2.4.1 requires typing-extensions~=3.7.4, but you have typing-extensions 3.10.0.2 which is incompatible.
Successfully installed PyYAML-5.3.1 click-7.1.2 joblib-1.0.0 lazypredict-0.2.9 lightgbm-2.3.1 more-itertools-8.10.0 numpy-1.19.1 pandas-1.0.5 pluggy-0.13.1 pytest-5.4.3 scikit-learn-0.23.1 scipy-1.5.4 six-1.15.0 tqdm-4.56.0 xgboost-1.1.1
WARNING: You are using pip version 21.2.4; however, version 21.3 is available.
You should consider upgrading via the '/root/venv/bin/python -m pip install --upgrade pip' command.
from lazypredict.Supervised import LazyClassifier, LazyRegressor
from sklearn.model_selection import train_test_split
/root/venv/lib/python3.7/site-packages/sklearn/utils/deprecation.py:143: FutureWarning: The sklearn.utils.testing module is deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.utils. Anything that cannot be imported from sklearn.utils is now part of the private API.
warnings.warn(message, FutureWarning)
# load data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2, random_state=42)
# fit all models
clf = LazyClassifier(predictions=True)
models, predictions = clf.fit(X_train, X_test, y_train, y_test)
100%|██████████| 29/29 [00:09<00:00, 3.05it/s]
models
predictions
success = np.where(df['raised_usd'] > 500000, 1, 0)
from matplotlib import pyplot
amount = df['token_for_sale'].fillna(0)
sns.regplot(x = amount, y = success, data = df,
logistic = True, y_jitter = .05)
plt.ylabel("success probability")
pyplot.xscale('log')
accepting = df['accepting'].str.count(",") + 1
sns.regplot(x = accepting, y = success, data = df,
logistic = True, y_jitter = .05)
plt.ylabel("success probability")
areas = df['restricted_areas'].str.count(",") + 1
sns.regplot(x = areas, y = success, data = df,
logistic = True, y_jitter = .05)
plt.ylabel("success probability")
sns.regplot(x = "teamsize", y = success, data = df,
logistic = True, y_jitter = .05)
plt.ylabel("success probability")
Q2: While keeping the Y-axis constant (the success probability), we can take a look at different X's that might suggest whether or not a coin will be successful. From the graphs above, we can see that as the rating goes higher, the success probability goes higher. Similarly, as the token for sale, accepting currencies, and team size gets larger, the success probability of a coin goes up. Conversely, as the number of restricted areas is higher, the success probability drops because less people can adopt the coin.
Q3: Variables that may be used from the data set to find meaningful X could be the country, is_ico and raised_usd, with success in Y, since such factors may help identify the performances of previous ICOs according to success and provide a guidance of successful and unsuccessful upcoming ICOs. Additionally, the heatmap helps visualize the different correlations between variables to see whether the data is meaningful and which combinations show a greater importance, hence providing a tool to make decisions on which variables should stand in X to be meaningful.
Q4: Firstly, we can use ‘is_ico’ to determine whether the data fits the scope, whether the offer is a subject for our study field. Secondly, we can compare the 'raised_usd' set with 500,000 to determine the success of the ICO. For a successful ICO, it should raise more than this amount. For countries, we can determine the success of different ICO in different countries based on the combination of ‘raised_usd’ and ‘country’ -the success of different ICO in different countries will be different due to different national policies, promotion degree and public opinion. So we might be able to find some possible connections by using the combination of ‘raised_usd’ and ‘country’.