Udemy - Using Data Science for Retail Store Segmentation

Files:

• Get Bonus Downloads Here.url (0.2 KB)
~Get Your Files Here ! 1 - Introduction
• 1 -Intro.pptx (289.8 KB)
• 1 -Introduction.mp4 (30.2 MB)
• 1 -Segmentation_Steps (1).ipynb (2.0 MB)
• 1 -Webscraping_Performance_Metrics (1).ipynb (26.8 KB)
• 1 -bigquery_code.sql (6.6 KB)
• 1 -sas_code.sas (12.4 KB)
• 2 -Business Problem.mp4 (7.1 MB)
• 2 -Business_Problem_Store_Segmentation.pdf (1.8 KB)
• 3 -Variables CU.pptx (1.2 MB)
• 3 -Variables.mp4 (14.0 MB)
10 - Describing Clusters (Python Version)
• 1 -Idea Decision Tree Feature Importance to Explain Membership to a Cluster.mp4 (19.8 MB)
• 1 -cluster_tree_profiling.pdf (28.7 KB)
• 2 -Function to plot distribution for a given variable (cluster vs population).mp4 (28.5 MB)
• 2 -plot_segment_vs_population_distribution.pdf (32.9 KB)
• 3 -Increasing the number of variables appearing in feature importance.mp4 (37.4 MB)
• 3 -tree_feature_importance_methods.pdf (42.1 KB)
• 4 -A robust method to choose top feature importances along several random states.mp4 (58.0 MB)
• 4 -robust_feature_importance_per_cluster.pdf (36.1 KB)
• 5 -Interpretation of segment 1.mp4 (24.7 MB)
• 5 -Segment_1_Analysis_Complete.pdf (640.4 KB)
• 6 -Interpretation of segment 2.mp4 (15.0 MB)
• 6 -Segment_2_Analysis_Complete.pdf (821.8 KB)
• 7 -Interpretation of segment 3.mp4 (12.9 MB)
• 7 -Segment_3_Analysis_Complete.pdf (477.8 KB)
• 8 -Interpretation of segment 4.mp4 (11.7 MB)
• 8 -Segment_4_Analysis_Complete.pdf (737.4 KB)
11 - Final Results
• 1 -Final Results.pptx (2.2 MB)
• 1 -Overview of the 4 segments.mp4 (30.6 MB)
• 1 -Step 1- preparing_data_presentation.pdf (43.2 KB)
• 1 -Step 2 distribution_life_cycle_plot.pdf (40.6 KB)
• 1 -Step 3 pie_chart_gender_segment_vs_population.pdf (37.8 KB)
• 1 -Step 4 distribution_plot_weather_segment_vs_population.pdf (39.4 KB)
• 1 -Step 5 preprocess_radar_chart_data.pdf (38.1 KB)
• 1 -Step 6 section_contribution_segment_vs_population.pdf (48.9 KB)
• 1 -metricas_x_segmento escalado.xlsx (15.2 KB)
• 1 -participacion_seccion_x_segmento.xlsx (12.5 KB)
• 2 -Segment 1 Tech-Focused Small Stores.mp4 (34.1 MB)
• 3 -Segment 2 Electronics & Furniture for Seniors.mp4 (13.4 MB)
• 4 -Segment 3 Comfort-Oriented Family stores.mp4 (11.4 MB)
• 5 -Segment 4 Single-Customer budget Stores.mp4 (8.1 MB)
• 6 -Comment On Cumulative Profit and Turnover.mp4 (7.6 MB)
2 - Data Extraction & Data Preprocessing
• 1 -Colab Connection to BQ (Extraction of Stores).mp4 (26.1 MB)
• 1 -colab connection.pdf (25.3 KB)
• 10 -Google Sheet (Stores Size) + Simple Preprocessing Processing.mp4 (28.4 MB)
• 10 -size_of_stores.pdf (25.2 KB)
• 11 -API Denue (Competition Around Stores).mp4 (108.8 MB)
• 11 -extracting_city_competition.pdf (44.2 KB)
• 2 -BigQuery Transactions.mp4 (37.1 MB)
• 2 -transactions_from_bigquery.pdf (34.7 KB)
• 3 -Existing Customer Segmentation.mp4 (34.9 MB)
• 3 -existing customer segmentation.pdf (43.4 KB)
• 4 -Webscraping.mp4 (128.6 MB)
• 4 -webscraping performance metrics.pdf (45.9 KB)
• 5 -Some Preprocessing On Output of Webscraping.mp4 (13.7 MB)
• 5 -some_preprocessing.pdf (22.1 KB)
• 6 -More Preprocessing On Output of Webscraping.mp4 (38.1 MB)
• 6 -more_preprocessing.pdf (24.5 KB)
• 7 -Cumulative Performance Metrics from Webscraping.mp4 (46.8 MB)
• 7 -more preprocesing cumulative metrics.pdf (39.3 KB)
• 8 -Economy of the City (BQ) + Some Preprocessing.mp4 (32.7 MB)
• 8 -environment_city_economy.pdf (19.6 KB)
• 9 -Weather (BQ) + Some Preprocessing.mp4 (9.5 MB)
• 9 -weather extraction.pdf (19.1 KB)
3 - Handling Missing Data & More Data Preprocessing
• 1 -Missing Values Due to Opening Dates.mp4 (40.4 MB)
• 1 -filter store and sections.pdf (276.6 KB)
• 2 -Intersecting Tables and Dataframes with Selected Stores and Sections.mp4 (42.4 MB)
• 2 -intersecting with relevant stores and sections.pdf (31.1 KB)
• 3 -Missing Values in Transactions Table.mp4 (53.2 MB)
• 3 -missing values in transactions.pdf (356.5 KB)
4 - Feature Engineering
• 1 -Contribution of Sales per Section.mp4 (24.2 MB)
• 1 -sales_per_section_percentage_contribution.pdf (25.2 KB)
• 2 -Contribution per Life Cycle.mp4 (19.1 MB)
• 2 -life_cycle_contribution.pdf (21.1 KB)
• 3 -Contribution per Gender.mp4 (8.8 MB)
• 3 -gender_contribution.pdf (19.7 KB)
• 4 -Average Sales, Profit and Inventory.mp4 (50.7 MB)
• 4 -monthly_averages_inventory_profit_sales.pdf (23.8 KB)
• 5 -Cumulative Turnover and Cumulative Profit.mp4 (12.1 MB)
• 5 -cumulative_performance_metrics.pdf (28.8 KB)
• 6 -Average Ticket, Average Spending and Average Credit Limit.mp4 (10.5 MB)
• 6 -gasto_ticket_lc_promedio_code.pdf (355.0 KB)
5 - Exploratory Analysis I (Descriptive Statistics)
• 1 -Top sections contributing to the total sales.mp4 (31.6 MB)
• 1 -top_sections.pdf (23.9 KB)
• 2 -Correlation Plots.mp4 (49.2 MB)
• 2 -correlation_plots.pdf (24.1 KB)
• 3 -Distribution Plots.mp4 (73.3 MB)
• 3 -distribution_plots.pdf (39.9 KB)
• 4 -Box plots.mp4 (28.7 MB)
• 4 -box_plots.pdf (27.0 KB)
6 - Data Transformation
• 1 -Winsorization, Yeo-Johnson Transformation and Standardization.mp4 (35.3 MB)
• 1 -data_transformation_functions.pdf (21.8 KB)
• 1 -transformations.pptx (1.2 MB)
• 2 -Transforming Top Sections.mp4 (36.2 MB)
• 2 -transform_top_sections.pdf (25.4 KB)
• 3 -Transforming Weather Variable.mp4 (9.5 MB)
• 3 -transform_weather.pdf (21.8 KB)
• 4 -Transforming Customer Variables.mp4 (18.0 MB)
• 4 -transform_customers.pdf (23.4 KB)
• 5 -Transforming Economy Variables.mp4 (12.0 MB)

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