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