Add CHANGELOG

CHANGELOG

+def print_imported_modules():
+    import sys
+    for name, val in sorted(sys.modules.items()):
+        if(hasattr(val, '__version__')): 
+            print(val.__name__, val.__version__)
+#        else:
+#            print(val.__name__, "(unknown version)")
+def print_sys_info():
+    import sys
+    import platform
+    print(sys.version)
+    print(platform.uname())
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import statsmodels.api as sm
+import seaborn as sns
+
+print_sys_info()
+print_imported_modules()
+
+data = pd.read_csv("data_shuttle.csv")
+data
+
+
+%matplotlib inline
+pd.set_option('mode.chained_assignment',None) # this removes a useless warning from pandas
+import matplotlib.pyplot as plt
+
+data["Frequency"]=data.Malfunction/data.Count
+data.plot(x="Temperature",y="Frequency",kind="scatter",ylim=[0,1])
+plt.grid(True)
+
+
+
+import statsmodels.api as sm
+
+data["Success"]=data.Count-data.Malfunction
+data["Intercept"]=1
+
+logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], 
+                family=sm.families.Binomial(sm.families.links.logit)).fit()
+
+logmodel.summary()
+
+
+
+
+logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], 
+                family=sm.families.Binomial(sm.families.links.logit),
+                var_weights=data['Count']).fit()
+
+logmodel.summary()
+
+
+
+
+%matplotlib inline
+data_pred = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121), 'Intercept': 1})
+data_pred['Frequency'] = logmodel.predict(data_pred)
+data_pred.plot(x="Temperature",y="Frequency",kind="line",ylim=[0,1])
+plt.scatter(x=data["Temperature"],y=data["Frequency"])
+plt.grid(True)
+
+
+
+
+sns.set(color_codes=True)
+plt.xlim(30,90)
+plt.ylim(0,1)
+sns.regplot(x='Temperature', y='Frequency', data=data, logistic=True)
+plt.show()
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