Portfolio Project: Restaurant Visitor Forecasting for Recruit
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Section 1: Overview
- Course Introduction and What You Will Learn
- How to Get Queries Resolved
- Download Resources
Section 2: Setup Environment
- Python Setup - Local Installation
- Python Setup - Google Colab
- Packages Used
Section 3: Understanding the data
- Data Overview
- Basic Data Stats
Section 4: Exploratory Data Analysis
- Course Review
- Exploratory Data Analysis
Section 5: Engineer new features and process data
- Feature Engineering - Domain Specific
- Feature Engineering - Interaction Features
- Course Review
Section 6: EDA - Engineered features & significance tests
- Exploratory Data Analysis Part 2
- ANOVA Concept- Intuition
- ANOVA Cancept - Maths
- Significance Tests
Section 7: Data pre-processing
- Label Encoding
- Data Preprocessing for Model Building
- Feature Encoding Approaches
- Course Review
Section 8: Evaluation methods for regression
- Evaluation Metrics
Section 9: Introduction to time series modeling
- Introduction to Time Series Modeling
- Time Series and Stationarity Concepts
- ACF and PACF
- Course Review
Section 10: Time series model - ARIMA
- Introduction to ARIMA Modeling
- AR and MA Models - Part 1
- AR and MA Models - Part 2
- Auto Arima concept
- Strategy 1 - ARIMA Forecasting Demo
- Strategy 1 - ARIMA Forecasting Demo - Part 2
- Durbin Watson Statistic
- Strategy 2 - Auto ARIMA
- Strategy 3 - AutoARIMA for Genre
Section 11: Time series model - SARIMA and SARIMAX
- SARIMA and SARIMAX
- Strategy 4 SARIMA Demo
- Strategy 5 SARIMAX Demo
- Exogenous Variables Deepdive Part 1 - Time Based and Demographics
- Exogenous Variables Deepdive Part 2 - Qualitative and Promotions
- Exogenous Variables Deepdive Part 3 - Series decomposition and
LifeCycle
- Exogenous Variables Deepdive Part 4 - Macro Economic Features
- Forecast for Unknown Future
Section 12: Time series model - Prophet by Facebook
- Prophet by Facebook
- Prophet Forecasting Demo
Section 13: Machine learning models - XGBoost and CATBoost
1 New Plan of Attack
2. XGBoost Demo
3. CatBoost
4. CatBoost Demo
5. Course Review
6. Hyper Parameters and Tuning
Section 14: Model improvement and interpretation
- Cohorted Ensembles
- Cohorted Ensembles Demo
Section 15: Conclusion
- Final Words
- Course Review