Create README.md

README.md

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+---
+task_categories:
+- image-classification
+- object-detection
+language:
+- en
+tags:
+- stationary
+- school
+- eraser
+- sharpner
+---
+# School Supplies Dataset
+
+This dataset contains a comprehensive collection of school supply products. It is a valuable resource for a variety of tasks, including market analysis, price comparison, and inventory management. Some example of products are eraser, pencil box, notebook, geometry etc.
+
+![Collage](image_collage.jpg "collage")
+
+## Data Description
+
+The dataset is structured as a single table (e.g., a CSV file) with the following columns:
+
+| Column Name            | Description                                                  | Data Type      |
+| ---------------------- | ------------------------------------------------------------ | -------------- |
+| **Product Name** | The name of the school supply product (e.g., "No. 2 Pencils, 12 Pack"). | String         |
+| **file\_name** | The corresponding image file name for the product.           | String         |
+| **Description** | A detailed textual description of the stationary item.          | String         |
+| **Article Id** | A unique identifier for the product.                         | String/Integer |
+| **Price** | The retail price of the stationary (in Rupee).         | Float/Integer  |
+| **Features** | Key features and characteristics of the product, often as a list. | String         |
+| **Brand** | The brand name of the stationary.                               | String         |
+| **Manufacturer** | The name of the manufacturer.                                | String         |
+| **Manufacturer Address**| The physical address of the manufacturer.                    | String         |
+| **Manufacturer Email** | The contact email for the manufacturer.                      | String         |
+| **Manufacturer Website**| The official website of the manufacturer.                    | String         |
+| **Sold By** | The retailer or seller of the product.                       | String         |
+| **Included Components**| Any additional items included with the purchase.             | String         |
+| **Country of Origin** | The country where the stationary was made (e.g., India).        | String         |
+| **Net Quantity** | The number of items in the product package.                  | Integer        |
+| **Product Type** | The category or type of stationary (e.g., Necklace, Earrings).  | String         |
+| **Height** | The height of the product (units may vary).                  | Float/String   |
+| **Length** | The length of the product (units may vary).                  | Float/String   |
+| **Width** | The width of the product (units may vary).                   | Float/String   |
+| **Product Url** | The URL to the product's listing page.                       | String         |
+
+---
+
+## Data Source
+
+The data has been aggregated from publicly available information on jiomart.com e-commerce websites.
+
+---
+
+## Potential Use Cases
+
+This dataset can be leveraged for a wide range of academic and commercial projects:
+
+* **Computer Vision**:
+    * **Image Classification**: Train models to automatically categorize stationary into types like *Pencil*, *Sharpners*, *Erasers* etc.
+    * **Object Detection**: Identify and locate specific stationary items within an image.
+* **Natural Language Processing (NLP)**:
+    * **Sentiment Analysis**: Analyze customer reviews (if descriptions contain them) or product descriptions to gauge sentiment.
+    * **Named Entity Recognition (NER)**
+    * **Text Generation**: Fine-tune language models to generate compelling product descriptions.
+* **Market Analysis**:
+    * **Price Prediction**: Build a model to predict the price of a stationary item based on its features.
+    * **Trend Analysis**: Identify popular styles, materials, and brands over time.
+* **Recommendation Systems**:
+    * Develop content-based filtering systems to recommend similar stationary to users based on product attributes.