365 Data Science - Customer Analytics in Python [CoursesGhar]

Creation Time	Sept. 3, 2023, 4:09 p.m.
Last Access Time	Nov. 27, 2024, 5:04 p.m.
File Size	795.2 MB
Keywords	Customer CoursesGhar Python Science - Analytics in 365 Data
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Total Files	64
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11. Deep Learning/4. Balancing the Dataset.mp4 44.6 MB
1. A Brief Marketing Introduction/4. Price Elasticity.mp4 32.1 MB
11. Deep Learning/5. Preprocessing the Data for Deep Learning.mp4 30.5 MB
2. Segmentation Data/2. Importing and Exploring Segmentation Data.mp4 30.0 MB
10. Modeling Purchase Quantity/2. Preparing the Data and Fitting the Model.mp4 29.5 MB
1. A Brief Marketing Introduction/2. Marketing Mix.mp4 28.2 MB
1. A Brief Marketing Introduction/1. Segmentation, Targeting, Positioning.mp4 27.7 MB
11. Deep Learning/7. Training the Deep Learning Model.mp4 27.0 MB
5. K-Means Clustering based on Principal Component Analysis/5. K-Means Clustering with Principal Components - Results.mp4 25.5 MB
1. A Brief Marketing Introduction/3. Physical and Online Retailers - Similarities and Differences..mp4 23.1 MB
11. Deep Learning/2. Exploring the Dataset.mp4 22.6 MB
6. Purchase Data/2. Getting to know the Purchase Dataset.mp4 20.4 MB
4. K-Means Clustering/3. K-Means Clustering - Results.mp4 20.2 MB
11. Deep Learning/11. Predicting on New Data.mp4 18.5 MB
8. Modeling Purchase Incidence/6. Purchase Probability by Segments.mp4 18.0 MB
11. Deep Learning/9. Obtaining the Probability of a Customer to Convert.mp4 17.1 MB
3. Hierarchical Clustering/2. Hierarchical Clustering - Implementation and Results.mp4 16.2 MB
9. Modeling Brand Choice/7. Own and Cross-Price Elasticity by Segment - Comparison.mp4 16.2 MB
9. Modeling Brand Choice/6. Own and Cross-Price Elasticity by Segment.mp4 16.2 MB
11. Deep Learning/8. Testing the Model.mp4 15.6 MB
7. Descriptive Analyses by Segments/4. Dissecting the revenue by segment.mp4 15.2 MB
8. Modeling Purchase Incidence/4. Calculating Price Elasticity of Purchase Probability.mp4 14.4 MB
9. Modeling Brand Choice/5. Cross Price Brand Choice Elasticity.mp4 14.1 MB
7. Descriptive Analyses by Segments/1. Purchase Analytics Descriptive Statistics - Segment Proportions.mp4 13.9 MB
8. Modeling Purchase Incidence/5. Price Elasticity of Purchase Probability - Results.mp4 12.5 MB
2. Segmentation Data/1. Getting to know the Segmentation Dataset.mp4 11.8 MB
7. Descriptive Analyses by Segments/3. Brand Choice.mp4 11.8 MB
9. Modeling Brand Choice/4. Own Price Brand Choice Elasticity.mp4 11.4 MB
11. Deep Learning/1. Introduction to Deep Learning for Customer Analytics.mp4 11.0 MB
4. K-Means Clustering/2. K-Means Clustering - Application.mp4 10.9 MB
10. Modeling Purchase Quantity/3. Calculating Price Elasticity of Purchase Quantity.mp4 10.7 MB
5. K-Means Clustering based on Principal Component Analysis/2. Principal Component Analysis - Application.mp4 10.5 MB
5. K-Means Clustering based on Principal Component Analysis/3. Principal Component Analysis - Results.mp4 9.9 MB
7. Descriptive Analyses by Segments/2. Purchase Analytics Descriptive Statistics - Purchase occasion and purchase Incidence.mp4 9.5 MB
6. Purchase Data/4. Applying the Segmentation Model.mp4 9.3 MB
8. Modeling Purchase Incidence/3. Model Estimation.mp4 9.2 MB
2. Segmentation Data/3. Standardizing Segmentation Data.mp4 9.2 MB
5. K-Means Clustering based on Principal Component Analysis/6. Saving the Models.mp4 8.8 MB
3. Hierarchical Clustering/1. Hierarchical Clustering - Background.mp4 8.8 MB
11. Deep Learning/6. Outlining the Deep Learning Model.mp4 8.5 MB
10. Modeling Purchase Quantity/1. Purchase Quantity Models. The Model - Linear Regression.mp4 7.9 MB
8. Modeling Purchase Incidence/9. Comparing Price Elasticities with and without Promotion.mp4 7.4 MB
4. K-Means Clustering/1. K-Means Clustering - Background.mp4 7.3 MB
5. K-Means Clustering based on Principal Component Analysis/4. K-Means Clustering with Principal Components - Application.mp4 7.0 MB
9. Modeling Brand Choice/1. Brand Choice Models. The Model - Multinomial Logistic Regression.mp4 7.0 MB
9. Modeling Brand Choice/3. Interpreting the Coefficients.mp4 6.7 MB
10. Modeling Purchase Quantity/4. Price Elasticity of Purchase Quantity - Results.mp4 6.6 MB
8. Modeling Purchase Incidence/1. Purchase Incidence Models. The Model - Binomial Logistic Regression.mp4 6.4 MB
8. Modeling Purchase Incidence/7. Purchase Probability Model with Promotion.mp4 6.0 MB
9. Modeling Brand Choice/2. Prepare Data and Fit the Model.mp4 5.2 MB
8. Modeling Purchase Incidence/8. Calculating Price Elasticities with Promotion.mp4 5.1 MB
11. Deep Learning/10. Saving the Model and Preparing for Deployment.mp4 4.4 MB
6. Purchase Data/3. Importing and Exploring Purchase Data.mp4 4.2 MB
5. K-Means Clustering based on Principal Component Analysis/1. Principal Component Analysis - Background.mp4 4.1 MB
6. Purchase Data/1. Purchase Analytics - Introduction.mp4 3.1 MB
11. Deep Learning/3. How Are We Going to Tackle the Business Case.mp4 3.0 MB
8. Modeling Purchase Incidence/2. Prepare the Dataset for Logistic Regression.mp4 3.0 MB
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365 Data Science - Customer Analytics in Python [CoursesGhar]

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