├── CarRentalSystem.py
└── College Data Project.py


/CarRentalSystem.py:
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 1 | class Vehicle:
 2 |   def __init__(self, make, model):
 3 |     self.make = make
 4 |     self.model = model
 5 | 
 6 |   def make_model(self):
 7 |     print(f"I'm a {self.make} {self.model}.")
 8 | 
 9 |   def moves(self):
10 |     print("Moves Going..")
11 |   
12 | mycar = Vehicle('Tesla', 'Model 3')
13 | 
14 | # print(mycar.make)
15 | # print(mycar.model)
16 | 
17 | mycar.make_model()
18 | mycar.moves()
19 | 
20 | 
21 | your_car = Vehicle('Civi', 'Newf4')
22 | your_car.make_model()
23 | your_car.moves()
24 | 
25 | class Airplane(Vehicle):
26 |   def moves(self):
27 |     print("File along..")
28 | 
29 | class Truck(Vehicle):
30 |   def moves(self):
31 |     print("Truck Moving...")
32 | 
33 | class moter(Vehicle):
34 |   def moves(self):
35 |     print("Bike Moving..")
36 | 
37 | your_Airplane = Airplane('Private', 'MB29')
38 | your_Truck = Truck('G19', 'Gulish')
39 | your_moter = moter('CD-70', 'Honda')
40 | 
41 | your_Airplane.make_model()
42 | your_Airplane.moves()
43 | 
44 | your_Truck.make_model()
45 | your_Truck.moves()
46 | 
47 | your_moter.make_model()
48 | your_moter.moves()
49 | 


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/College Data Project.py:
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  1 | # -*- coding: utf-8 -*-
  2 | """SYED MUSTAFA HASSAN - SP24 - Intro to ML - Assignment 01 .ipynb
  3 | 
  4 | Automatically generated by Colaboratory.
  5 | 
  6 | Original file is located at
  7 |     https://colab.research.google.com/drive/15gsUQpiyq1jreVcQqD-HLdumltL-3OYi
  8 | 
  9 | #Problem Introduction
 10 | This exercise relates to the College data set, which can be found in
 11 | the file College.csv on the book website. It contains a number of
 12 | variables for 777 different universities and colleges in the US. The
 13 | variables are
 14 | * Private : Public/private indicator
 15 | * Apps : Number of applications received
 16 | * Accept : Number of applicants accepted
 17 | * Enroll : Number of new students enrolled
 18 | * Top10perc : New students from top 10 % of high school class
 19 | * Top25perc : New students from top 25 % of high school class
 20 | * F.Undergrad : Number of full-time undergraduates
 21 | * P.Undergrad : Number of part-time undergraduates
 22 | * Outstate : Out-of-state tuition
 23 | * Room.Board : Room and board costs
 24 | * Books : Estimated book costs
 25 | * Personal : Estimated personal spending
 26 | * PhD : Percent of faculty with Ph.D.s
 27 | * Terminal : Percent of faculty with terminal degree
 28 | * S.F.Ratio : Student/faculty ratio
 29 | * perc.alumni : Percent of alumni who donate
 30 | * Expend : Instructional expenditure per student
 31 | * Grad.Rate : Graduation rate
 32 | 
 33 | Before reading the data into Python, it can be viewed in Excel or a
 34 | text editor.
 35 | 
 36 | 
 37 | ---
 38 | 
 39 | **Q.1**
 40 | Use the pd.read_csv() function to read the data into Python. Call
 41 | the loaded data college. Make sure that you have the directory
 42 | set to the correct location for the data
 43 | """
 44 | 
 45 | import numpy as np
 46 | import pandas as pd
 47 | 
 48 | Auto = pd.read_csv('College.csv')
 49 | Auto
 50 | 
 51 | """**Q.2**
 52 | Look at the data used in the notebook by creating and running
 53 | a new cell with just the code college in it. You should notice
 54 | that the first column is just the name of each university in a
 55 | column named something like Unnamed: 0. We don’t really want
 56 | pandas to treat this as data. However, it may be handy to have
 57 | these names for later. Try the following commands and similarly
 58 | look at the resulting data frames:
 59 | 
 60 | 
 61 | 
 62 | ```
 63 | college2 = pd.read_csv('College.csv', index_col=0)
 64 | college3 = college.rename({'Unnamed: 0': 'College'},
 65 | axis=1)
 66 | college3 = college3.set_index('College')
 67 | ```
 68 | 
 69 | 
 70 | 
 71 | This has used the first column in the file as an index for the
 72 | data frame. This means that pandas has given each row a name
 73 | corresponding to the appropriate university. Now you should see
 74 | that the first data column is Private. Note that the names of
 75 | the colleges appear on the left of the table. We also introduced
 76 | a new python object above: a dictionary, which is specified by
 77 | (key, value) pairs. Keep your modified version of the data with
 78 | the following:
 79 | 
 80 | 
 81 | ```
 82 | college = college3
 83 | ```
 84 | 
 85 | 
 86 | 
 87 | """
 88 | 
 89 | # college2 = pd.read_csv('College.csv', index_col=0)
 90 | # college3 = college.rename({'Unnamed: 0': 'College'},
 91 | # axis=1)
 92 | # college3 = college3.set_index('College')
 93 | 
 94 | college2 = pd.read_csv('College.csv', index_col=0)
 95 | 
 96 | college2.index.name = 'College'
 97 | 
 98 | college = college2
 99 | 
100 | college
101 | 
102 | """**Q.3**
103 | Use the describe() method of to produce a numerical summary
104 | of the variables in the data set.
105 | """
106 | 
107 | datasummary = college.describe()
108 | 
109 | datasummary
110 | 
111 | """**Q.4**
112 | Use the pd.plotting.scatter_matrix() function to produce a
113 | scatterplot matrix of the first columns [Top10perc, Apps, Enroll].
114 | Recall that you can reference a list C of columns of a data frame
115 | A using A[C].
116 | """
117 | 
118 | import matplotlib.pyplot as plt
119 | 
120 | pd.plotting.scatter_matrix(college[['Top10perc', 'Apps', 'Enroll']])
121 | plt.show()
122 | 
123 | """**Q.5**
124 | Use the boxplot() method of college to produce side-by-side
125 | boxplots of Outstate versus Private.
126 | """
127 | 
128 | college.boxplot(column='Outstate', by='Private')
129 | plt.show()
130 | 
131 | """**Q.6**
132 | Create a new qualitative variable, called Elite, by binning the
133 | Top10perc variable into two groups based on whether or not the
134 | proportion of students coming from the top 10% of their high
135 | school classes exceeds 50%.
136 | 
137 | 
138 | ```
139 | college['Elite'] = pd.cut(college['Top10perc'],
140 | [0,0.5,1],
141 | labels=['No', 'Yes'])
142 | ```
143 | Use the value_counts() method of college['Elite'] to see how
144 | many elite universities there are. Finally, use the boxplot() method
145 | again to produce side-by-side boxplots of Outstate versus Elite.
146 | 
147 | """
148 | 
149 | college['Elite'] = pd.cut(college['Top10perc'], [0, 50, 100], labels=['No', 'Yes'])
150 | elite_counts = college['Elite'].value_counts()
151 | elite_boxplot = college.boxplot(column='Outstate', by='Elite')
152 | 
153 | plt.show()
154 | 
155 | """**Q.7**
156 | Use the plot.hist() method of college to produce some histograms with differing numbers of bins for a few of the quantitative variables. The command plt.subplots(2, 2) may be useful: it will divide the plot window into four regions so that four
157 | plots can be made simultaneously. By changing the arguments
158 | you can divide the screen up in other combinations.
159 | """
160 | 
161 | fig, axes = plt.subplots(2, 2)
162 | college['Apps'].plot.hist(ax=axes[0,0], bins=10)
163 | college['Accept'].plot.hist(ax=axes[0,1], bins=20)
164 | college['Enroll'].plot.hist(ax=axes[1,0], bins=15)
165 | college['Top10perc'].plot.hist(ax=axes[1,1], bins=5)
166 | plt.show()


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