servoprediction.py

# -*- coding: utf-8 -*-
"""ServoPrediction.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1lBICidYiX7vbXBoWcry4qjGiuUcimWAD

# **SERVO PREDICTION WITH LINEAR REGRESSION **

DATA SOURCE:'https://github.com/YBIFoundation/Dataset/raw/main/Servo%20Mechanism.csv

**IMPORT LIBRARY**
"""

import pandas as pd
import numpy as np

"""**IMPORT DATA**"""

servo=pd.read_csv('https://github.com/YBIFoundation/Dataset/raw/main/Servo%20Mechanism.csv')

"""**DESCRIBE DATA**"""

servo.head()

servo.info()

servo.describe()

"""**DATA VISUALIZATION**"""

servo.columns

servo.shape

servo['Motor'].value_counts()

servo['Screw'].value_counts()

servo.replace({'Motor':{'A':0,'B':1,'C':2,'D':3,'E':4}},inplace=True)

servo.replace({'Screw':{'A':0,'B':1,'C':2,'D':3,'E':4}},inplace=True)

y=servo['Class']

y.shape

y

X=servo[['Motor', 'Screw','Pgain', 'Vgain']]

X=servo.drop(['Class'],axis=1)

X

X.shape

"""**Train Test Split**"""

from sklearn.model_selection import train_test_split

X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=2529)

X_train.shape,X_test.shape,y_train.shape,y_test.shape

"""**Modeling**"""

from sklearn.linear_model import LinearRegression
lr=LinearRegression()

lr.fit(X_train,y_train)

"""**Prediction**"""

y_pred=lr.predict(X_test)

y_pred.shape

y_pred

"""Model Evaluation"""

from sklearn.metrics import mean_squared_error,mean_absolute_error,r2_score

mean_squared_error(y_test,y_pred)

mean_absolute_error(y_test,y_pred)

r2_score(y_test,y_pred)

import matplotlib.pyplot as plt
plt.scatter(y_test,y_pred)
plt.xlabel('Actual')
plt.ylabel('Predicted')
plt.title("Actual vs Predicted")
plt.show()