/Student-Percentage-based-on-study-hours

Primary LanguageJupyter Notebook

1-Importing Data And Initial Exploring

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

%matplotlib inline
# importing data and exploring
df = pd.read_csv('./student_scores.csv')
print(df.shape)
print(df.describe())
df.head()
(25, 2)
           Hours     Scores
count  25.000000  25.000000
mean    5.012000  51.480000
std     2.525094  25.286887
min     1.100000  17.000000
25%     2.700000  30.000000
50%     4.800000  47.000000
75%     7.400000  75.000000
max     9.200000  95.000000
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Hours Scores
0 2.5 21
1 5.1 47
2 3.2 27
3 8.5 75
4 3.5 30 
# Plotting variables and checking correlation
df.plot(
    x='Hours',
    y='Scores',
    style='x',
    xlabel='Score %',
    ylabel='hours studied',
    title='Hours vs Percentage'
)

print(df.corr())
           Hours    Scores
Hours   1.000000  0.976191
Scores  0.976191  1.000000

png

There is a strong positive correlation of 97.6% between the 2 variables

2- Creating Train and Test Dastasets

X = df.iloc[:,:-1].values
y = df.iloc[:,-1].values
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error as MSE
X_train, X_test, y_train, y_test = train_test_split(
    X,
    y,
    test_size=0.2,
    random_state=42
)
model = LinearRegression()
model.fit(X_train, y_train)
LinearRegression()

# ploting Regression line and test data

plt.scatter(X,y)
plt.plot(X, ((model.coef_ * X) + model.intercept_)) # y=mx+c
[<matplotlib.lines.Line2D at 0x7fd4e911c730>]

png

3-Predictions

y_pred = model.predict(X_test)
pd.DataFrame({'Actual':y_test, 'predicted':y_pred})
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Actual predicted
0 81 83.188141
1 30 27.032088
2 21 27.032088
3 76 69.633232
4 62 59.951153 
MSE(y_test, y_pred)
3.9207511902099244

4- Predicting Score For 9.25 Hours

prediction = model.predict(np.array(9.25).reshape(1,-1))

print('for studying for 9.25 hours it is predicted to score: ' + str(round(*prediction,2)) + '%')
for studying for 9.25 hours it is predicted to score: 92.39%