The Spark Foundation

By: Karan

Task-1 Prediction using Supervised ML

Problem statment

● predicted score if a student studies for 9.25 hrs/ day

● dataset: http://bit.ly/w-data

libraries required

In [25]:
import pandas as pd 
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

Importing data

In [26]:
url=('https://raw.githubusercontent.com/AdiPersonalWorks/Random/master/student_scores%20-%20student_scores.csv')
stud=pd.read_csv(url)
stud
Out[26]:
Hours Scores
0 2.5 21
1 5.1 47
2 3.2 27
3 8.5 75
4 3.5 30
5 1.5 20
6 9.2 88
7 5.5 60
8 8.3 81
9 2.7 25
10 7.7 85
11 5.9 62
12 4.5 41
13 3.3 42
14 1.1 17
15 8.9 95
16 2.5 30
17 1.9 24
18 6.1 67
19 7.4 69
20 2.7 30
21 4.8 54
22 3.8 35
23 6.9 76
24 7.8 86
In [27]:
# checking for null values
stud.isnull().sum()
Out[27]:
Hours     0
Scores    0
dtype: int64

Hours vs Score

In [4]:
sns.lineplot(x="Hours",y="Scores",data=stud, color='red')
plt.show()

outliers detection

In [5]:
sns.boxplot(y="Hours", data=stud)
plt.show()
In [6]:
sns.boxplot(y="Scores", data=stud)
plt.show()

Most time spent by students

In [7]:
stud['Hours'].value_counts().plot(kind= 'bar')
Out[7]:
<matplotlib.axes._subplots.AxesSubplot at 0x1d3ab718>

Most scored by students

In [8]:
stud['Scores'].value_counts().plot(kind= 'bar')
Out[8]:
<matplotlib.axes._subplots.AxesSubplot at 0x1d40f6a0>

Observations:

Higher the hours spent the scores are good in number
no outier detected
max spent hours is 2.5 and 2.7
max scored is 30

Dividing the data features into X,y variables

In [9]:
X = stud.iloc[:,:1]
y = stud.iloc[:,1]

Imported the sklearn library train_test split

In [10]:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size = 0.7, test_size = 0.3, random_state = 100)
In [11]:
X_train.head()
Out[11]:
Hours
6 9.2
12 4.5
4 3.5
24 7.8
0 2.5
In [12]:
y_train.head()
Out[12]:
6     88
12    41
4     30
24    86
0     21
Name: Scores, dtype: int64
In [13]:
from sklearn.linear_model import LinearRegression

# Representing LinearRegression as lr(Creating LinearRegression Object)
lm = LinearRegression()

# Fit the model using lr.fit()
lm.fit(X_train, y_train)
Out[13]:
LinearRegression()

Print the parameters, i.e. the intercept and the slope of the regression line fitted

In [14]:
print(lm.intercept_)
print(lm.coef_)

1.4951421092364043
[9.87171443]

plotting the best fit line

In [15]:
plt.scatter(X_train, y_train)
plt.plot(X_train, 1.495142109236383 + 9.87171443*X_train, 'y')
plt.title("Prediction")
plt.xlabel("Hours")
plt.ylabel("Scores")
plt.show()

Predict the y values corresponding to X_test_sm

In [16]:
y_pred = lm.predict(X_test)
In [17]:
print(y_pred)

[28.14877107 39.00765694 34.07179972 59.73825724 16.30271375 74.54582888
 69.60997167 48.87937137]

comparing the actual versus predicted

In [18]:
comparison=pd.DataFrame({"Actual":y_test,"Predicted":y_pred})
comparison
Out[18]:
Actual Predicted
9 25 28.148771
22 35 39.007657
13 42 34.071800
11 62 59.738257
5 20 16.302714
19 69 74.545829
23 76 69.609972
21 54 48.879371

plotting the best fit line for the test dataset

In [19]:
plt.scatter(X_test, y_test)
plt.plot(X_test, 1.495142109236383 + 9.87171443 * X_test, 'y')
plt.show()

printing the predicted score

In [20]:
pred_score = lm.predict([[9.25]])
print("The predicted score is :",pred_score)

The predicted score is : [92.80850057]

Returns the mean squared error; we'll take a square root

In [22]:
np.sqrt(mean_squared_error(y_test, y_pred))
Out[22]:
5.067387845160843
In [23]:
r_squared = r2_score(y_test, y_pred)
r_squared
Out[23]:
0.9309458862687439
In [24]:
mean_absolute_error(y_test, y_pred)
Out[24]:
4.762517892332273