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COMM5007 Coding for business
创建日期：2024-05-25 17:23:21
标签： COMM5007
Coding for business

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COMM5007 Coding for business

3Copyright
• There are some file-sharing websites that specialise in buying and selling
academic work to and from university students.
• If you upload your original work to these websites, and if another student
downloads and presents it as their own either wholly or partially, you
might be found guilty of collusion — even years after graduation.
• These file-sharing websites may also accept purchase of course
materials, such as copies of lecture slides and tutorial handouts. By law,
the copyright on course materials, developed by UNSW staff in the
course of their employment, belongs to UNSW. It constitutes copyright
infringement, if not academic misconduct, to trade these materials.
4Country
• UNSW Business School acknowledges the
Bidjigal (Kensington campus) and Gadigal
(City campus) the traditional custodians of
the lands where each campus is located.
• We acknowledge all Aboriginal and Torres
Strait Islander Elders, past and present and
their communities who have shared and
practiced their teachings over thousands of
years including business practices.
• We recognize Aboriginal and Torres Strait
Islander people’s ongoing leadership and
contributions, including to business,
education and industry.
UNSW Business School. (2022, August 18). Acknowledgement of Country [online video].
Retrieved from https://vimeo.com/369229957/d995d8087f
5
6Predictive Modelling on Classification
7Revision
•Regression Analysis using Python – An Example
8Simple Linear Regression Example
•A real estate agent wishes to examine the relationship
between the selling price of a home and its size (measured in
square feet)
• "kc_house_data.csv" dataset
9Simple Linear Regression Example:
Regression Line
•House price vs living area: regression line
10Simple Linear Regression Example:
Regression Line
•House price vs living area: regression line
import pandas as pd
import numpy as np
from sklearn import linear_model
import matplotlib.pyplot as plt
df = pd.read_csv("kc_house_data.csv")
regr = linear_model.LinearRegression()
df['price_inMillion'] = df["price"]/1000000
price = df[['price_inMillion']]
livingArea = df[['sqft_living']]
regr.fit(livingArea, price)
predictPrice = regr.coef_[0][0]*livingArea + regr.intercept_[0]
plt.plot(np.array(livingArea), np.array(predictPrice), '-r')
plt.xlabel("Living Area-Square Footage of the Room")
plt.ylabel("Price (in Million)")
plt.title('Price vs Living Area', fontsize = 14)
Estimated_Price = 0.00028 * sqft_living – 0.04358
11Simple Linear Regression Example:
Regression Line
•House price vs living area: scatter plot and regression line
12Simple Linear Regression Example:
Regression Line
•House price vs living area: scatter plot and regression line
import pandas as pd
import numpy as np
from sklearn import linear_model
import matplotlib.pyplot as plt
df = pd.read_csv("kc_house_data.csv")
regr = linear_model.LinearRegression()
df['price_inMillion'] = df["price"]/1000000
price = df[['price_inMillion']]
livingArea = df[['sqft_living']]
regr.fit(livingArea, price)
predictPrice = regr.coef_[0][0]*livingArea + regr.intercept_[0]
plt.plot(np.array(livingArea), np.array(predictPrice), '-r')
plt.scatter(df.sqft_living, df.price_inMillion, color = 'Black')
plt.xlabel("Living Area-Square Footage of the Room")
plt.ylabel("Price (in Million)")
plt.title('Price vs Living Area', fontsize = 14)

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CIVL2010 ECMT2150 CS 333 CP1406 KIT308 Economics 701 ECON3740 ECE 5759 BFC3340 FINC2012 COMP4500 MATH3871 FIT3080 18-819C Economic ECO202Y5Y PHY100 FINC-780 Econ 100B EC602 IE582 EE450 COMP9517 WRDS 150B ECON 570 LECTURE 2 ECA5101 ECE 4420 ADAD9311 FIT3031 CS 544 MF 703 CS 6140 KIT501 ENGSCI 344 CSC171 IE 332 FINM8007 21S2 COSC 1114 FE545 E471 SIT102 CSC108 MAT301 IAE 101 COP3503 STAT2004 EC 303 CS1026 STAT1203 MATH3171 ISOM5160 IFN657 FIT2014 ACCT2102 DSCI550 STATS 330 ELEC421 COMP3670 QBUS6840 MEES:698B MAST30027 MIET1077 Economics IFT 6758 FIT5122 BUSN5101 CptS 111 HRMT5502 Math2018 PP 312 INFS3200 CZ3005 CSSE1001 SENG 2011 MKTG 553Q BUFN 640 COMP507 ETF5952 DATA 311 ECE4043 BANK3011 finance M3H623544 HW1 INFS 7410 AI6103 STAT3888 INFS3208 ACCT7103 ECOS3013 PSYCH 20B F79MA STAT3006 ENGG952 2X MGSC 7000 ECE4132 MSF 526 MATH 3033 CDS 511 EEEE4120 EEET2465 ECA5103 MATH2070 ELEC3104 BU51019 EEE8147 ENGR20003 COSC 1107 ETF3200 FNCE 435 MSFI 435 ISyE6420 GENG4405 HW ETC5523 IE 7315 MOEC0021 STA 106 ENGG 1300A ENGG 1300 OLET1139 STAT 3503 STAT 206 FEM11149 ELEN 4720 COMP20003 SOLA1070 CS574 C11CS LINB29H3 MAST90083 ECON7200 STAT 610 LINB29 DSM-5 COMP3161 EEEN40010 MATH 4431 K353 STA 3386 IERG4300 COMS W4167 ENG5298 AMME 2302 CS3910 COMPSCI 320 STATS 369 GR5242 COSC 328 CSCI435 MGTS1301 INFS2200 COMP3900 CIS 375 Module AE03 COMP3121 CS260 HAM503 MGMT5050 INT3075 COSC 285 GEOL0029 GEGE2X01 STAT1201 MATH1061 MMF1941H STAT 5060 CS 520 COMP 4320 MSIN0104 000T FINS3648 ECMT2160 ECON4003 Phys 129L CSE 142 CS 3130 MGEB01 EECS 340 Finance (20443/20444) ECMM149 COMP5310 EEEN60180 CSC 111 AT2 CSCI 4430 STAB57 MA/CSC 427 MATH322 D100 CSCI544 CITS3004 CI6227 CSE 565 APCO 1P01 852L1 ELEC S411F COMP3207 STATS330 ECON7530 FIT5149 CSI4104 CMPT 371 COMP201 SEHS4696 FI4003 MATH6182 CG1 WS MATH401 MGT 205 QTM 110 COMP 0137 MACE12201 FIT5210 ECE 544 BISM7206 HUDM 6055 ECON3203 STATS 210 MSCI 570 MATH6173 COMPSCI4039 Accounting COM3503 AOE2024 ECON213 PSYC10004 Control Data 100/200A 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COMP2013 IB9X60 INVP001 EMATM0051 COMP220 BLAW1002 E20 TCP8001 ECON61001 EART40005 ECONM1022 MATH 365 MATH 367 ELEC6218 COMP 5812M EDUC 5061M ELEC ENG 1101 equation MATH6174 ST334 C4 EC9570 A33648 EBUS603 ECON47815 stata WFMS0001 PEM102 INF6060 ionically ELECTRICAL MATH 375 MMW 12 MKTG860 ECON60401 ALY6010 SOST20131 Introduction COM3524 MME2 GGR305 JNB 538 EECS101 COMM5030 IEOR E4601 2B CSC336 EN2315 LAW 432 CSC263 MATH2001 OLET1143 CSCB63 SIT772 COMM1170 STAT 441 MS930 INFT3050 GLBH0030 Stat 120B MAST10007 MGEC61 Machine GGR 252H1S ECON7030 Financial Marketing MGMT30019 ECON5120 SDSC 8009 CSC165H1S DSCI 551 3081W ACM41000 ECA 5304 MATH 1064 ECSE444 DATA 604 COMP7105 MIE1615 ECON4004 ENV200H1S EC481 MKT 306 EC1B1 MATH08051 ALY6140 CS260C CSE30 MSIN0226 CS4103 CSC3064 AM16 COMP11212 CSE4101 PSTAT 174 Math 380W CNT 5106C STAT 310 FIN2028 CS5014 STAT 425 COMP 4102A ELEC2140 MANF9544 QRM 9 QRM 8 ECON 457 COMPSCI 4091 MATH 523 DNSC-4211 PEAS 6000 MSIN0180 COMP2119 A2A HT 2022 IB93F0 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