Delete models

models/README.md

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-# Model Card for Product Return Prediction
-
-## model details
-
-- **person or organization developing model**: team product-return-prediction
-- **model date**: 24/11/2024
-- **model version**: v1.4
-- **model type**: Support Vector Machine
-
-<!-- algorithm description -->
-This model is a **Support Vector Machine** classifier designed to predict whether a product will be returned or not, based on various product and transaction features. Hyperparameters (C, kernel type and gamma) are chosen using a grid search, with a 10-fold cross validation.
-
-## intended use
-
-### primary intended uses
-
-<!-- description of the model's use -->
-The purpose of the model is to assist e-commerce owners (Armani) in identifying possible returns among their purchases in order to reorganize inventories to optimize product handling and transportation costs
-
-### primary intended users
-
-<!-- description of the users -->
-The model was developed for Armani. Specifically, the purpose is to support professional figures involved in logistics, product management, and marketing
-
-<!-- ### out-of scope use cases -->
-
-## factors
-
-### relevant factors
-
-<!-- factors to consider -->
-Some factors to be considered that involve the model are the following:
-
-- **product features**: characteristics like model, fabric, colour, composition, and product category may have a significant impact on the likelihood of a product being returned
-- **imbalanced classes**: the class imbalance is a relevant factor that may affect the model's ability to predict the minority class (returns) accurately
-
-### decision thresholds
-
-<!-- description of selected thresholds -->
-The default decision threshold for the SVM model is 0.5, where probabilities greater than or equal to 0.5 indicate a "returned" prediction, and probabilities below 0.5 indicate "not returned."
-
-## Train and Test data
-
-### dataset description
-
-- **dataset**: *German Sales 2023 EA*
-
-the model was trained and tested on this dataset, following appropriate splitting and pre-processing steps. 
-
-### split
-
-Dataset splitting is as follows:
-- **training**: 80%
-- **validation and test**: 20%
-
-the splitting is performed by using the corresponding sklearn function. The chosen random state is 42.
-
-### pre-processing
-
-To be adapted to the binary classification task, and further adapted to a numerical model such as SVM, the model underwent an important pre-processing phase. Pre-processing steps are the following:
-
-1.  Dataset conversion from Excel to TSV
-2. Specific columns removal from dataframe
-3. Train and test data splitting
-4. Train and save scaler
-5. Scaling data with a pre-trained scaler
-6. Target encoding of categorical columns
-7. Preparation of inventory with sales data
-8. Population of missing values
-9. Calculation and application of return percentages by color
-10. Final cleaning and processing
-
-## Quantitative analysis
-
-|           | PRECISION | RECALL    | F1-SCORE  | Support   |
-|-----------|-----------|-----------|-----------|-----------|
-| No return | 0.95      | 0.95      | 0.95      | 2086      |
-| Return    | 0.89      | 0.90      | 0.89      | 960       |
-| Accuracy  |           |           |           |0.93       |
-
-
-<!-- ### unitary results -->
-
-<!-- ### intersectional results -->
-

models/scaler.pkl

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models/svm.pkl

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