import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

podaci={
    "Temperatura":[15,18,20,22,25,28,30,32],
    "Prodaja":[20,25,30,35,45,55,65,75]
}
df=pd.DataFrame(podaci)
print(df)

prosek=np.mean(df["Prodaja"])
print("Prosecna prodaja: ",prosek)

X=df[["Temperatura"]]
y=df["Prodaja"]
model=LinearRegression()
model.fit(X,y)
df["Predvidjena_prodaja"]=model.predict(X)
print(df)

plt.scatter(df["Temperatura"],df["Prodaja"],label="Stvarna prodaja")
plt.plot(df["Temperatura"],df["Predvidjena_prodaja"],color="red",label="Predvidjena prodaja")
plt.xlabel("Temperatura")
plt.ylabel("Prodaja")
plt.legend()
plt.show()
----------------------------------------------------------------

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression

podaci = {
    "Sati_ucenja": [1, 2, 2, 3, 4, 5, 6, 7, 8, 9],
    "Izostanci": [10, 8, 7, 6, 5, 4, 3, 2, 2, 1],
    "Polozio": [0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
}

df = pd.DataFrame(podaci)
print(df)

print("Prosečan broj sati:", np.mean(df["Sati_ucenja"]))
print("Broj položenih:", np.sum(df["Polozio"]))

X = df[["Sati_ucenja", "Izostanci"]]
y = df["Polozio"]

model = LogisticRegression()
model.fit(X, y)

novi_ucenik = pd.DataFrame({
    "Sati_ucenja": [5],
    "Izostanci": [2]
})

rezultat = model.predict(novi_ucenik)

if rezultat[0] == 1:
    print("Učenik će položiti.")
else:
    print("Učenik neće položiti.")

plt.scatter(df["Sati_ucenja"], df["Izostanci"],
            c=df["Polozio"], cmap="coolwarm")

plt.xlabel("Sati učenja")
plt.ylabel("Izostanci")
plt.title("Uspeh učenika")
plt.show()

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