Question

use the attached data set to plot graph using regression in python

4 1 fixed acid volatile ac citric acid residual sichlorides free sulfu total sulfu density pHsulphates alcoholquality type 34 0.9978 3.51 0.56 1.9 0.076 0 2.6 0.098 25 5 red 3.2 0.68 7.8 0.88 67 0.9968 54 0.997 3.26 0.65 17 60 0.998 3.16 0.58 9.8 7.8 0.760.04 2.3 0.092 11.20.28 1.9 0.075 0 1.9 0.076 11 34 0.9978 3.51 0.569.4 5 red 7.4 0.66 0 1.8 0.05 13 400.99783.510.569.4 1.6 0.069 59 0.9964 3.3 0.46 7.3 0.65 0 1.2 0.065 15 21 0.99463.390.47 7 red 7.8 2 0.073 7.5 0.5 0.36 6.1 0.071 0.58 0.02 18 0.99683.36 0.57 102 0.9978 3.35 0.810.5 0.58 0.08 3.280.54 65 0.9959 7.5-0.5 0.36 6.1 0.071 17 102 0.99783.35 10.5 0.615 1.6 0.089 59 0.9943 3.58 0.52 7.8 0.61 0.29 1.6 0.114 8.9 0.62 0.18 3.8 0.176 52145 0.99863.160.889.2 29 0.9974 3.26 1.56 5 red 8.9 0.620.193.90.17 51 148 0.9986 3.170.93 8.5 0.28 0.56 1.8 0.092 35 103 0.9969 3.3 0.75 10.5 7 red 81 0.56 0.28 170.368 56 0.9968 3.11 1.28 0.59 0.08 4.4 0.086 29 0.9974 4 red .9 0.32 0 .51 1.8 0.341 56 0.9969 3.04 1.08 6 red 8.9 0.220.48 1.8 0.077 60 0.9968 3.39 0.53 6 red wine-data (1) elect destination and press ENTER or choose Paste 0 Type here to search

Answer 1

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

# read data to datsframe
data = pd.read_csv("alchol.csv")
# linear regression class instance
model = LinearRegression()
# fit the model training
model.fit(data[["fixed acid","volatie acid","citric acid","residual sulfur","chlorides","free sulfur","total sulfur","density","pH","sulphates"]],data.quality)
# coefficients of function
print(model.coef_)
# plotting the graphs with each variants to qulaity
plt.subplot(2,5,1)
plt.title("quality vs fixed acid")
plt.plot(data["fixed acid"],model.coef_[0]*data["fixed acid"])
plt.subplot(2,5,2)
plt.title("quality vs volatie acid")
plt.plot(data["volatie acid"],model.coef_[1]*data["volatie acid"])
plt.subplot(2,5,3)
plt.title("quality vs citric acid")
plt.plot(data["citric acid"],model.coef_[2]*data["citric acid"])
plt.subplot(2,5,4)
plt.title("quality vs residual sulfur")
plt.plot(data["residual sulfur"],model.coef_[3]*data["residual sulfur"])
plt.subplot(2,5,5)
plt.title("quality vs chlorides")
plt.plot(data["chlorides"],model.coef_[4]*data["chlorides"])
plt.subplot(2,5,6)
plt.title("quality vs free sulfur")
plt.plot(data["free sulfur"],model.coef_[5]*data["free sulfur"])
plt.subplot(2,5,7)
plt.title("quality vs total sulfur")
plt.plot(data["total sulfur"],model.coef_[6]*data["total sulfur"])
plt.subplot(2,5,8)
plt.title("quality vs density")
plt.plot(data["density"],model.coef_[7]*data["density"])
plt.subplot(2,5,9)
plt.title("quality vs pH")
plt.plot(data["pH"],model.coef_[8]*data["pH"])
plt.subplot(2,5,10)
plt.title("quality vs sulphates")
plt.plot(data["sulphates"],model.coef_[9]*data["sulphates"])
plt.show()

quality vs fixed acid quality vs volatie acid quality vs citric acid quality vs residual sulfur quality vs chlorides 0.10 0.5 02 0.09 0.4 0.4 0.08 0.3 04 0.6 05 0.07 1.0 06 0.8 0.06 1.2 0.05 0.0 08 0.4 0.3 0.2 0.4 0.6 0.8 0.2 quality vs sulphates 6 10 0.0 0.2 0.1 quality vs free sulfur quality vs total sulfur quality vs density quality vs pH 3.3 02 2.5 3.4 -234.4 03 1.0 3.5 2.0 -234.6 04 3.6 1.5 1.5 -234.8 3.7 05 2.0 1.0 -235.0 3.8 2.5 06 0.5 3.9 20 50 100 150 0.996 0.998 3.2343.65indows 0,5 Go to PC settings to activate Windows