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Introduction to Market Basket Analysis

Association rules

Parameters of association rules

Apriori algorithm

Fpgrowth algorithm

Exploratory Data Analysis

Univariate analysis

Bivariate analysis

Identifying top selling products & departments

Creating baskets for analysis

Feature engineering

One hot encoding

Difference between apriori and fpgrowth algorithm

Support, lift, confidence in relation to association rules

Comparing time taken to run apriori and fpgrowth algorithms

Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.

The goal of this data science project is to take an image of a handwritten single digit, and determine what that digit is.

Build a predictive model to correctly classify products between 9 product categories (fashion, electronics, etc.) using the Otto Group dataset.

Introduction to Market Basket Analysis

05m

Introduction To Association Rules

03m

Loading And Understanding the Dataset

09m

Exploratory Data Analysis

06m

Univariate Analysis

12m

Bivariate Analysis

03m

Creating Order Products

07m

Department wise featured products

06m

Theory About Apriori And Fpgrowth

06m

Filtering And One Hot Encoding

08m

Apriori Algorithm

07m

Deeper Understanding Of Association Rules

05m

General Function Using Apriori Association Rules

05m

Fpgrowth Algorithm

03m

General Function Using Fpgrowth

04m

Comparison Of Apriori And Fpgrowth Conclusion

05m