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oleumer.py
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oleumer.py
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from PIL import Image
im = Image.open("images.jpg")
colors = im.getpixel((143,92))
print(colors)
if (100 < colors[0] and 70 < colors[1] < 150):
print("ORANGE DETECTED")
df = pd.read_csv("Tester.csv")
plt.xlabel('Age')
plt.ylabel('BPM')
plt.scatter(df.Age, df.BPM, color = 'red', marker = '+')
#Linear
reg = linear_model.LinearRegression()
reg.fit(df[['Age']], df.BPM)
#print(reg.predict([[3]])[0])
#Logisitic
X_Train, X_Test, Y_Train, Y_Test = train_test_split(df[['Age']], df.BPM, test_size = 12)
#print(X_Test)
model = LogisticRegression()
model.fit(X_Train, Y_Train)
#print('-')
#print(model.predict_proba(X_Test))
#print(model.predict(X_Test))
#model = Keras.Sequential([keras.layers.Dense(10, input_shape(784,), activation='sigmoid')])
#model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])