### Linear Regression Lab

Implementation of Linear Regression Using Gradient Descent

Please go through my previous article before this lab work.And i will provide dataset to download.

first

SOURCE CODE:

#used to get top five rows in the dataset

df.describe()

df.isnull().sum()

df.info()

x=df.Hours

y=df.Scores

iterations=1000

n=len(df)

n

n=len(df)

c=0

m=0

L=0.01

loss = []

for i in range(1000):

y_pred=m * x + c

MSE=(1/n)*sum((y_pred-y)**2)

dm=(2/n)*sum(x*(y_pred-y))

dc=(2/n)*sum((y_pred-y))

c=c-L*dc

m=m-L*dm

loss.append(MSE)

print(m,c)

ypred = m*x+c

ypred

plt.scatter(x,y,color="blue")

plt.plot(x,y_pred)

plt.xlabel("Study hours")

plt.ylabel("Scores")

plt.title("Study hours vs Scores")

plt.plot(loss)

plt.xlabel("iterations")

plt.ylabel("loss")

Output:

DATA_DESCRIBE  :

m & c :

GRAPHS:

And if you get any doubt please comment below.And go through this link which I have executed in Google Colab.

In the
given link the above code available

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