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Referring to the output of describe() method, what is the range of the attribute population?
describe()
From 3 to 787
From 3 to 1166
From 3 to 1725
From 3 to 35682
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1 Introduction
2 Machine Learning Pipeline
3 Pipeline MCQ
4 Look at the big picture
5 MCQ Big Picture
6 Objective and Benefits
7 Current Solution
8 Choosing solution MCQ
9 Frame The Problem
10 Supervised Learning
11 Create a music app
12 Movie recommendation- Supervised or not
13 Unsupervised Learning
14 Movie recommendation- Unsupervised or not
15 Suggest songs
16 Reinforcement Learning
17 Bike Rental Forecasting
18 Facial recognition
19 Facial Recognition-2
20 Facial Recognition- 3
21 Learning Type
22 Sub-domains of supervised learning
23 quiz classification or regression
24 Performance Measure
25 Check the Assumptions
26 Virtual Environment
27 Installing Libraries
28 Get the Data
29 Explore the Dataset
30 Quick description of the dataset
31 Total Instances
32 Missing Values
33 Non-Numeric Values
34 Identifying categorical attributes
35 Categories
36 Statistical description
37 Understanding statistical description
38 Standard Deviation
39 Range
40 Plotting histogram
41 Magic functions
42 Importing pyplot
43 Histograms
44 Conclusions from Histogram
45 Create a test set
46 Stratified sampling
47 Split criteria
48 StratifiedShuffleSplit
49 Create an object of StratifiedShuffleSplit
50 Generating test set- Splitting
51 Generating Test Set-Indices
52 Generating test set- Locating data
53 Conclusion
54 Explore the Data to gain Insights
55 copy() method
56 Visualizing geographical data
57 Visualizing the target variable
58 Insights from the plot
59 Looking for correlations
60 Computing correlation matrix
61 Correlation Matrix - 1
62 Correlation Matrix -2
63 Correlation matrix - 3
64 Scatter Matrix
65 Attribute with the most correlation
66 Attribute combinations
67 New Dataset
68 Prepare the Data for Machine Learning Algorithms
69 Missing values
70 Missing values -1
71 SimpleImputer
72 Handling categorical and text attributes
73 One Hot Encoding categories
74 Custom Transformers
75 Creating Custom Transformers
76 Feature Scaling
77 StandardScaler
78 Transformation Pipelines
79 ColumnTransformer
80 Exploring models
81 LinearRegression
82 Measuring error
83 Training a powerful model
84 Overfitting
85 Cross Validation
86 Performing cross Validation
87 Evaluating RandomForestRegressor
88 Observing Random Forests
89 Fine tune the model
90 GridSearchCV
91 Result of grid search
92 Evaluating the model on the test set
93 Analyzing model
94 Present the solution
95 Launch, monitor and maintain the system
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