Machine Learning: Simple Linear Regression with Python Example- To predict students scores based on their study hours

Question

Prediction using Supervised Machine Learning using Simple Linear Regression

In this task we have to find the students scores based on their study hours. This is a simple Regression problem type because it has only two variables. 

Sample Dataset- you can download a csv file of dataset

Answer 1

GO_STP_6266

https://www.linkedin.com/posts/sahil-parmar-4099391bb_google-colaboratory-activity-6805454270809415680-c71D

Answer 2

import pandas as pd

data = pd.read_csv("E:\python practice data\StudentHoursScores.csv")

# In[3]:

print(data.head())

print(data.tail())

print(data.shape)

print(data.dtypes)

print(data.columns)

print(data.corr())

# In[4]:

data.describe()

# In[5]:

type(data)

# In[13]:

x = data.iloc[:,0:-1]

y = data.iloc[:,-1]

print(x.head())

print(y.head())

print(x.shape,y.shape)

# In[14]:

from sklearn.model_selection import train_test_split

x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=.2,random_state=1)

print(x_train.shape)

print(x_test.shape)

print(y_train.shape)

print(y_test.shape)

# In[15]:

from sklearn.linear_model import LinearRegression

std_model  = LinearRegression()

std_model.fit(x_train,y_train)

# In[17]:

y_pred = std_model.predict(x_test)

print(y_pred)

print(y_test)

print(x_test)

# In[22]:

print(std_model.coef_)

print(std_model.intercept_)

from sklearn.metrics import r2_score, mean_squared_error

print("accuracy is: ", r2_score(y_test,y_pred))

print(mean_squared_error(y_test,y_pred))

# In[24]:

vif = 1/1-(r2_score(y_test,y_pred))

vif

coef:  [9.9073553]
intercept: 0.2569567372371395
accuracy is:  0.9311770139374417
mean squared error: 3.7164412473781487