GRIP-task

The Spark Foundation - GRIP task 1

Data Science and Business Analyst Intern

Problem statement : Predict the percentage of students based on the number of study hours.

Import Libraries and Dataset

import numpy as np

import pandas as pd

import seaborn as sns

import matplotlib.pyplot as plt

from matplotlib import style

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LinearRegression

from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error

from sklearn.metrics import accuracy_score

from math import sqrt

get_ipython().run_line_magic('matplotlib', 'inline')

students = pd.read_excel(r"C:\Users\Swapnil\OneDrive\Desktop\Machine Learning\datasets\gripStudents.xlsx")

students.head()

students.describe()

students.isna().sum()

plt.scatter(students.Hours, students.Scores, color="c")

#plt.plot(height, weight, color="c")

plt.xlabel("Hours")

plt.ylabel("Scores")

plt.show()

correlation = students.corr()

sns.heatmap(correlation, annot = True)

fig = plt.figure(1, figsize=(12,7))

Finding and treating outliers

ax = fig.add_subplot(111)

bp = ax.boxplot(students, patch_artist=True)

Splitting the data for Training and Testing

X = students[['Hours']]

y = students[['Scores']]

x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

linear = LinearRegression()

model = linear.fit(x_train, y_train) model

y_pred = model.predict(x_test)

plt.scatter(x_train, y_train, color='black')

sns.regplot(x_train, linear.predict(x_train), color='r')

plt.title("Hour studied Vs Scores obtained") plt.xlabel('Hours') plt.ylabel('Scores') plt.grid(True,color='white', linestyle='-.')

y_pred

h = int(input())

predictions = model.predict([[h]]) print("If a student studying",h,"Hrs daily, then score will be",predictions[0])

pred2 = model.predict([[9.25]])

print("If students studies 9.25 hrs/day then predicted score of that students will be", pred2)

RMSE = float(format(np.sqrt(mean_squared_error(y_test, y_pred)),'.3f'))

MSE = format(mean_squared_error(y_test, y_pred), '.3f')

r2 = format(r2_score(y_test, y_pred),'.4f')

print('Root Mean Squared Error =',RMSE, '\nMean Squared Error =',MSE,'\nR2 Score =', r2)

mean_absolute_error(y_test, y_pred)

This model gives 95.68 % of accuracy