Linear_SVC_machine_learning_and_testing_our_data_part_one.py

## Linear SVC machine learning and testing our data

import numpy as np

import matplotlib.pyplot as plt
from sklearn import svm
import pandas as pd

from matplotlib import style
style.use("ggplot")

##defined a function to build our dataset
def Build_Data_Set(features = ["DE Ratio",
                               "Trailing P/E"]):

##loaded our data to the variable data_df

    data_df = pd.DataFrame.from_csv("key_stats1.csv")

##extracted first 100 rows of the data
    
    data_df = data_df[:100]

##we fill the X parameter with the NumPy array containing rows of features
    
    X = np.array(data_df[features].values)

##we replace our status column with numerical data
    
    y = (data_df["Status"]
         .replace("underperform",0)
         .replace("outperform",1)
         .values.tolist())

##finally we return X and y
    
    return X,y

##Analysing and visualising our data

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()