p13.py

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
import pandas as pd
from matplotlib import style
style.use("ggplot")

def Build_Data_Set(features = ["DE Ratio", "Trailing P/E"]):
  data_df = pd.DataFrame.from_csv("key_stats.csv")
  data_df = data_df[:100]
  X = np.array(data_df[features].values)
  y = ( data_df["Status"]
        .replace("underperform",0)
        .replace("outperform",1)
        .values.tolist()
  )
  return X,y

def Analysis():
  X, y = Build_Data_Set()
  clf = svm.SVC(kernel="linear", C= 1.0)
  clf.fit(X,y)
  w = clf.coef_[0]
  a = -w[0] / w[1]
  xx = np.linspace(min(X[:, 0]), max(X[:, 0]))
  yy = a * xx - clf.intercept_[0] / w[1]

  h0 = plt.plot(xx,yy, "k-", label="non weighted")

  plt.scatter(X[:, 0],X[:, 1],c=y)
  plt.ylabel("Trailing P/E")
  plt.xlabel("DE Ratio")
  plt.legend()
  plt.show()
  
Analysis()