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365 Data Science - Customer Analytics in Python [CoursesGhar]

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795.21 MB

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28-06-2021 12:21

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File	Size
!! IMPORTANT Note !!.txt	298.00 B
!!! Please Support !!! [CoursesGhar.Com].txt	197.00 B
00. Websites You May Like/A1movies.com.pk.url	116.00 B
00. Websites You May Like/CoursesGhar.com.url	114.00 B
1. A Brief Marketing Introduction/1. Segmentation, Targeting, Positioning.mp4	27.75 MB
1. A Brief Marketing Introduction/2. Marketing Mix.mp4	28.19 MB
1. A Brief Marketing Introduction/3. Physical and Online Retailers - Similarities and Differences..mp4	23.15 MB
1. A Brief Marketing Introduction/4. Price Elasticity.mp4	32.14 MB
10. Modeling Purchase Quantity/1. Purchase Quantity Models. The Model - Linear Regression.mp4	7.86 MB
10. Modeling Purchase Quantity/2. Preparing the Data and Fitting the Model.mp4	29.55 MB
10. Modeling Purchase Quantity/3. Calculating Price Elasticity of Purchase Quantity.mp4	10.73 MB
10. Modeling Purchase Quantity/4. Price Elasticity of Purchase Quantity - Results.mp4	6.60 MB
11. Deep Learning/1. Introduction to Deep Learning for Customer Analytics.mp4	10.95 MB
11. Deep Learning/10. Saving the Model and Preparing for Deployment.mp4	4.41 MB
11. Deep Learning/11. Predicting on New Data.mp4	18.52 MB
11. Deep Learning/2. Exploring the Dataset.mp4	22.59 MB
11. Deep Learning/3. How Are We Going to Tackle the Business Case.mp4	3.02 MB
11. Deep Learning/4. Balancing the Dataset.mp4	44.65 MB
11. Deep Learning/5. Preprocessing the Data for Deep Learning.mp4	30.54 MB
11. Deep Learning/6. Outlining the Deep Learning Model.mp4	8.53 MB
11. Deep Learning/7. Training the Deep Learning Model.mp4	27.01 MB
11. Deep Learning/8. Testing the Model.mp4	15.63 MB
11. Deep Learning/9. Obtaining the Probability of a Customer to Convert.mp4	17.07 MB
2. Segmentation Data/1. Getting to know the Segmentation Dataset.mp4	11.83 MB
2. Segmentation Data/2. Importing and Exploring Segmentation Data.mp4	30.00 MB
2. Segmentation Data/3. Standardizing Segmentation Data.mp4	9.15 MB
3. Hierarchical Clustering/1. Hierarchical Clustering - Background.mp4	8.78 MB
3. Hierarchical Clustering/2. Hierarchical Clustering - Implementation and Results.mp4	16.20 MB
4. K-Means Clustering/1. K-Means Clustering - Background.mp4	7.29 MB
4. K-Means Clustering/2. K-Means Clustering - Application.mp4	10.91 MB
4. K-Means Clustering/3. K-Means Clustering - Results.mp4	20.24 MB
5. K-Means Clustering based on Principal Component Analysis/1. Principal Component Analysis - Background.mp4	4.07 MB
5. K-Means Clustering based on Principal Component Analysis/2. Principal Component Analysis - Application.mp4	10.53 MB
5. K-Means Clustering based on Principal Component Analysis/3. Principal Component Analysis - Results.mp4	9.86 MB
5. K-Means Clustering based on Principal Component Analysis/4. K-Means Clustering with Principal Components - Application.mp4	6.99 MB
5. K-Means Clustering based on Principal Component Analysis/5. K-Means Clustering with Principal Components - Results.mp4	25.48 MB
5. K-Means Clustering based on Principal Component Analysis/6. Saving the Models.mp4	8.82 MB
6. Purchase Data/1. Purchase Analytics - Introduction.mp4	3.10 MB
6. Purchase Data/2. Getting to know the Purchase Dataset.mp4	20.37 MB
6. Purchase Data/3. Importing and Exploring Purchase Data.mp4	4.23 MB
6. Purchase Data/4. Applying the Segmentation Model.mp4	9.31 MB
7. Descriptive Analyses by Segments/1. Purchase Analytics Descriptive Statistics - Segment Proportions.mp4	13.95 MB
7. Descriptive Analyses by Segments/2. Purchase Analytics Descriptive Statistics - Purchase occasion and purchase Incidence.mp4	9.47 MB
7. Descriptive Analyses by Segments/3. Brand Choice.mp4	11.79 MB
7. Descriptive Analyses by Segments/4. Dissecting the revenue by segment.mp4	15.19 MB
8. Modeling Purchase Incidence/1. Purchase Incidence Models. The Model - Binomial Logistic Regression.mp4	6.40 MB
8. Modeling Purchase Incidence/2. Prepare the Dataset for Logistic Regression.mp4	3.01 MB
8. Modeling Purchase Incidence/3. Model Estimation.mp4	9.22 MB
8. Modeling Purchase Incidence/4. Calculating Price Elasticity of Purchase Probability.mp4	14.36 MB
8. Modeling Purchase Incidence/5. Price Elasticity of Purchase Probability - Results.mp4	12.52 MB
8. Modeling Purchase Incidence/6. Purchase Probability by Segments.mp4	18.05 MB
8. Modeling Purchase Incidence/7. Purchase Probability Model with Promotion.mp4	6.04 MB
8. Modeling Purchase Incidence/8. Calculating Price Elasticities with Promotion.mp4	5.13 MB
8. Modeling Purchase Incidence/9. Comparing Price Elasticities with and without Promotion.mp4	7.39 MB
9. Modeling Brand Choice/1. Brand Choice Models. The Model - Multinomial Logistic Regression.mp4	6.98 MB
9. Modeling Brand Choice/2. Prepare Data and Fit the Model.mp4	5.19 MB
9. Modeling Brand Choice/3. Interpreting the Coefficients.mp4	6.67 MB
9. Modeling Brand Choice/4. Own Price Brand Choice Elasticity.mp4	11.37 MB
9. Modeling Brand Choice/5. Cross Price Brand Choice Elasticity.mp4	14.13 MB
9. Modeling Brand Choice/6. Own and Cross-Price Elasticity by Segment.mp4	16.16 MB
9. Modeling Brand Choice/7. Own and Cross-Price Elasticity by Segment - Comparison.mp4	16.17 MB
Join Our Telegram Group For More Updates !!!.url	138.00 B
Uploaded by [Coursesghar.com].txt	1.10 kB
Visit coursesghar.com for more awesome tutorials.url	114.00 B

Description

365 Data Science - Customer Analytics in Python [CoursesGhar]Course description

Customer Analytics in Python is where marketing and data science meet. Data science and marketing are two of the key driving forces that help companies create value and stay on top in today’s fast-paced economy. This course is packed with knowledge, and includes sections on customer and purchase analytics, as well as a deep-learning model, all implemented in Python.