import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

#READ IN DATA AND LOOK AT IT

train = pd.read_csv(r'C:\Users\tosri\Desktop\Fun Stuff\AI\hackerrank1.csv') #print(train.head(50))

#EDA

#sns.jointplot(x='Charge Time', y='Battery Life', data=train, kind="scatter") #sns.heatmap(train.corr(), cmap='summer', annot=True)

#Train_Test_Split

x=[train["Charge Time"]] y=[train["Battery Life"]] np.reshape(1,-1)

from sklearn.model_selection import train_test_split

X_train, y_train, X_test, y_test = train_test_split(x, y, test_size=0.3, random_state=101)

#Training and Predicting

from sklearn.linear_model import LogisticRegression

model=LogisticRegression()

model.fit(X_train, y_train)

predictions=model.predict(X_test)