Portfolio Project: Restaurant Visitor Forecasting for Recruit

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Section 1: Overview

  1. Course Introduction and What You Will Learn
  2. How to Get Queries Resolved
  3. Download Resources

Section 2: Setup Environment

  1. Python Setup - Local Installation
  2. Python Setup - Google Colab
  3. Packages Used

Section 3: Understanding the data

  1. Data Overview
  2. Basic Data Stats

Section 4: Exploratory Data Analysis

  1. Course Review
  2. Exploratory Data Analysis

Section 5: Engineer new features and process data

  1. Feature Engineering - Domain Specific
  2. Feature Engineering - Interaction Features
  3. Course Review

Section 6: EDA - Engineered features & significance tests

  1. Exploratory Data Analysis Part 2
  2. ANOVA Concept- Intuition
  3. ANOVA Cancept - Maths
  4. Significance Tests

Section 7: Data pre-processing

  1. Label Encoding
  2. Data Preprocessing for Model Building
  3. Feature Encoding Approaches
  4. Course Review

Section 8: Evaluation methods for regression

  1. Evaluation Metrics

Section 9: Introduction to time series modeling

  1. Introduction to Time Series Modeling
  2. Time Series and Stationarity Concepts
  3. ACF and PACF
  4. Course Review

Section 10: Time series model - ARIMA

  1. Introduction to ARIMA Modeling
  2. AR and MA Models - Part 1
  3. AR and MA Models - Part 2
  4. Auto Arima concept
  5. Strategy 1 - ARIMA Forecasting Demo
  6. Strategy 1 - ARIMA Forecasting Demo - Part 2
  7. Durbin Watson Statistic
  8. Strategy 2 - Auto ARIMA
  9. Strategy 3 - AutoARIMA for Genre

Section 11: Time series model - SARIMA and SARIMAX

  1. SARIMA and SARIMAX
  2. Strategy 4 SARIMA Demo
  3. Strategy 5 SARIMAX Demo
  4. Exogenous Variables Deepdive Part 1 - Time Based and Demographics
  5. Exogenous Variables Deepdive Part 2 - Qualitative and Promotions
  6. Exogenous Variables Deepdive Part 3 - Series decomposition and LifeCycle
  7. Exogenous Variables Deepdive Part 4 - Macro Economic Features
  8. Forecast for Unknown Future

Section 12: Time series model - Prophet by Facebook

  1. Prophet by Facebook
  2. 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

  1. Cohorted Ensembles
  2. Cohorted Ensembles Demo

Section 15: Conclusion

  1. Final Words
  2. Course Review
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