English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 124 Lessons (8h 45m) | 1.56 GB
Time Series Forecasting with Python: Zero to Mastery
This project-based course will put you in the role of a Business Data Analyst at Airbnb tasked with predicting demand for Airbnb property bookings in New York. To accomplish this goal, you’ll use the Python programming language to build a powerful tool that utilizes the magic of time series forecasting.
The world is changing. Every decision must be made faster, smarter, and more accurately. And that’s possible with business data analysis. You’ll learn new skills and put them to the test in this project that puts you in the hypothetical scenario of a Business Data Analyst at Airbnb tasked with predicting demand for Airbnb properties in New York. You’ll use Python to build a tool utilizing time series forecasting to help Airbnb make the best, most accurate decision, and have a portfolio project that you can use to impress employers.
WHAT YOU’LL LEARN
- How to utilize the power of time series forecasting to predict the future
- How to use the four most relevant forecasting models used by Business Data Analysts today
- Practice the day-to-day skills needed for Business Data Analysis
- Build an impressive project to add to your portfolio to help you get hired
- Enhance your proficiency with Python, one of the most popular programming languages
Table of Contents
1 Course Introduction
2 Course Material
3 Why Forecasting Matters
4 Game Plan
5 TIme Series Data
6 Case Study Briefing
7 Python – Directory and Libraries
8 Python – Loading the Data
9 Python – Renaming Variable
10 Python – Summary Statistics
11 Additive vs. Multiplicative Seasonality
12 Python – Seasonal Decomposition
13 Python – Seasonal Graphs
14 Python – Visualization – Basic Plot
15 Python – Visualization – Customization
16 Python – Visualization -Adding Events
17 Python – Correlation
18 Auto-Correlation Plots
19 Python – Auto-Correlation Plot
20 Python – Useful Commands Template
21 Facebook Prophet Game Plan
22 Structural Time Series and Facebook Prophet
23 Python – Preparing the Script
24 Python – Date Variable
25 Python – Easter
26 Python – Thanksgiving
27 Python – Wrapping Up the Events
28 Facebook Prophet Parameters
29 Facebook Prophet Model
30 Cross-Validation
31 Python – Cross-Validation
32 Assessing Model Errors
33 Python – Cross-Validation Performance and Plot
34 Parameter Tuning
35 Python – Parameter Grid
36 Python – Parameter Tuning
37 Python – Best Parameters and Exporting
38 Python – Building Script
39 Python – Preparing Data Sets
40 Python – Final Facebook Prophet Model
41 Python – Forecasting
42 Python – Exporting Forecast
43 Facebook Prophet Pros and Cons
44 SARIMAX Game Plan
45 ARIMA
46 Python – Preparing Script
47 Auto-Regressive
48 Integrated
49 Python – Stationarity and Differencing
50 Moving Average Component
51 Optimization Factors
52 Python – SARIMAX Model
53 Python – Cross-Validation
54 Python – Parameter Grid
55 Python – Parameter Tuning
56 Python – Exporting Best Parameters
57 Python – Preparing the Script
58 Python – Preparing Data
59 Python – Tuned SARIMAX Model
60 Python – Forecasting
61 Python – Visualization and Export
62 SARIMAX Pros and Cons
63 LinkedIn Silverkite Game Plan
64 LinkedIn Silverkite
65 Silverkite vs. Prophet
66 Python – Libraries and Data
67 Python – Preparing Data
68 Python – Metadata
69 Silverkite Components
70 Growth Terms
71 Python – Growth Terms
72 Seasonality Terms
73 Python – Seasonality
74 Python – Available Countries and Holidays
75 Python – Holidays
76 Python – Changepoints
77 Python – Regressors
78 Lagged Regressors
79 Python – Lagged Regressors
80 Python – Autoregression
81 Fitting Algorithms Possibilities
82 Ridge Regression
83 XGBoost
84 Boosting
85 Feature Sampling
86 Python – Custom Fit Algorithm
87 Python – Silverkite Model
88 Python – Cross-Validation Configuration
89 Python – SIlverkite Parameter Tuning
90 Python – Visualization and Preparing Results
91 Python – Exporting Best Parameters
92 Python – Preparing Script
93 Python – Best Parameters and Silverkite Model
94 Python – Summary and Visualization
95 Python – Exporting Forecasts
96 Pros and Cons
97 Recurrent Neural Networks (RNN) Long Short-Term Memory (LSTM) Game Plan
98 Simple Neural Network
99 Recurrent Neural Networks (RNN)
100 Long Short-Term Memory (LSTM)
101 Python – Libraries and Data
102 Python – Time Series Objects
103 Python – Time Variables
104 Python – Scaling Variables
105 LSTM Parameters
106 Python – LSTM Model
107 Python – Cross-Validation
108 Python – CV Performance
109 Python – Parameter Grid
110 Python – Parameter Tuning (Round 1)
111 Python – Parameter Tuning (Round 2)
112 Python – Parameter Tuning (Final Results)
113 Python – Preparing Script
114 Python – Preparing Inputs
115 Python – Tuned LSTM Model
116 Python – Predictions and Exporting
117 LSTM Pros and Cons
118 Ensemble Game Plan
119 Ensemble Mechanism
120 Python – Preparing Script and Loading Predictions
121 Python – Loading Errors
122 Python – Forecasting Weights
123 Python – Ensemble Forecast and Visualization
124 Ensemble Pros and Cons
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