Unable to run logistic regression due to "perfect separation error"
<p>I'm a beginner to data analysis in Python and have been having trouble with this particular assignment. I've searched quite widely, but have not been able to identify what's wrong. </p>

<p>I imported a file and set it up as a dataframe. Cleaned the data within the file. However, when I try to fit my model to the data, I get a </p>

<blockquote>
  <p>Perfect separation detected, results not available</p>
</blockquote>

<p>Here is the code:</p>

<pre><code>from scipy import stats
import numpy as np
import pandas as pd 
import collections
import matplotlib.pyplot as plt
import statsmodels.api as sm

loansData = pd.read_csv('https://spark-   public.s3.amazonaws.com/dataanalysis/loansData.csv')

loansData = loansData.to_csv('loansData_clean.csv', header=True, index=False)

## cleaning the file
loansData['Interest.Rate'] = loansData['Interest.Rate'].map(lambda x:  round(float(x.rstrip('%')) / 100, 4))
loanlength = loansData['Loan.Length'].map(lambda x: x.strip('months'))
loansData['FICO.Range'] = loansData['FICO.Range'].map(lambda x: x.split('-'))
loansData['FICO.Range'] = loansData['FICO.Range'].map(lambda x: int(x[0]))
loansData['FICO.Score'] = loansData['FICO.Range']

#add interest rate less than column and populate
## we only care about interest rates less than 12%
loansData['IR_TF'] = pd.Series('', index=loansData.index)
loansData['IR_TF'] = loansData['Interest.Rate'].map(lambda x: True if x &lt; 12 else False)

#create intercept column
loansData['Intercept'] = pd.Series(1.0, index=loansData.index)

# create list of ind var col names
ind_vars = ['FICO.Score', 'Amount.Requested', 'Intercept'] 

#define logistic regression
logit = sm.Logit(loansData['IR_TF'], loansData[ind_vars])

#fit the model
result = logit.fit()

#get fitted coef
coeff = result.params

print coeff
</code></pre>

<p>Any help would be much appreciated!</p>

<p>Thx,
A</p>