Update README.md

README.md

@@ -5,9 +5,9 @@ datasets:
 language:
 - en
 metrics:
-- r_squared
-- mae
-- mse
+- r-squared
+- RMSE
+- MAE
 pipeline_tag: tabular-regression
 library_name: sklearn
 tags:
@@ -17,197 +17,137 @@ tags:
 - demand-forecasting
 - inventory-management
 ---
-# Model Card for Model ID
+# Smart Inventory Advisor for Retail Sales
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+This repository contains a machine learning model trained to predict daily sales for retail products and provide actionable inventory recommendations. The project was developed as part of the Datathon Nexus Crew.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
+The core of this project is a **Random Forest Regressor** (`scikit-learn`) trained to forecast the number of units sold (`Sales`) for a given product based on a variety of features. The primary output is a "Store Owner's Action Plan" that identifies products at risk of stocking out and recommends a reorder quantity.
 
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Developed by:** jerewy (NexusCrew)
+- **Model type:** `RandomForestRegressor`
+- **Language(s) (NLP):** en
+- **License:** MIT
+- **Finetuned from model [optional]:** This model was trained from scratch.
 
 ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [https://github.com/jerewy/datathon_nexus_crew](https://github.com/jerewy/datathon_nexus_crew)
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
+This model is intended to be used as a decision-support tool for small to medium-sized retail business owners. It helps answer two key questions:
+1.  **Which products should I focus on restocking right now?**
+2.  **How many units of each product should I order?**
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+The primary function, `get_smart_inventory_recommendations` in the notebook, automates this process.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model is not suitable for real-time stock market prediction or long-term (multi-year) financial forecasting. Its predictions are based on the patterns in the provided dataset and may not be accurate if underlying market conditions change drastically. The model should not be used for making automated financial decisions without human oversight.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+The main limitation of this model is the "predictability ceiling" of the source data. The final model achieves a Testing R² score of **0.34**, which means that while it successfully captures the predictable patterns, about 66% of the variance in daily sales is due to unpredictable, random factors not present in the data.
 
-[More Information Needed]
+- **Data Dependency:** The model's performance is entirely dependent on the quality and patterns of the training data. It assumes future trends will resemble historical ones.
+- **Synthetic Data:** The model was trained on a synthetic dataset. Performance on real-world, noisy retail data will likely differ and may require further tuning.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Users should be aware that the model provides a forecast, not a guarantee. The "Recommended Reorder Qty" should be treated as a strong, data-driven suggestion, but business owners should still apply their own domain knowledge, especially when considering external factors not included in the dataset (e.g., upcoming local events, new competitors).
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+Use the code below to load the saved model and preprocessors to make predictions on new data.
 
-[More Information Needed]
+```python
+import pickle
+import pandas as pd
 
-## Training Details
+# Load the trained model, scaler, and encoders
+model = pickle.load(open('sales_model.pkl', 'rb'))
+scaler = pickle.load(open('scaler.pkl', 'rb'))
+label_encoders = pickle.load(open('label_encoders.pkl', 'rb'))
 
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+# --- Example: Prepare a single row of new data ---
+# NOTE: This must have the same structure as the training data
+new_data = pd.DataFrame([{
+    'Inventory': 200, 'Orders': 50, 'Price': 35.0, 'Discount': 10, 
+    'Competitor Price': 33.0, 'Promotion': 1, 'Category': 'Groceries', 
+    'Region': 'North', 'Weather': 'Sunny', 'Seasonality': 'Spring',
+    'DayOfWeek': 2, 'Month': 4, 'Day': 15
+}])
 
-[More Information Needed]
+# Apply the same preprocessing
+for col in ['Category', 'Region', 'Weather', 'Seasonality']:
+    new_data[col] = label_encoders[col].transform(new_data[col])
 
-### Training Procedure
+# Scale the features
+new_data_scaled = scaler.transform(new_data)
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+# Make a prediction
+predicted_sales = model.predict(new_data_scaled)
+print(f"Predicted Sales: {predicted_sales[0]:.2f} units")
+```
 
-#### Preprocessing [optional]
+## Training Details
 
-[More Information Needed]
+### Training Data
 
+The model was trained on the "Retail Store Inventory" dataset, which contains over 73,000 daily records across multiple stores and products. The data is synthetic but realistically models retail sales patterns.
 
-#### Training Hyperparameters
+### Training Procedure
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+#### Preprocessing
 
-#### Speeds, Sizes, Times [optional]
+The training data was preprocessed as follows:
+1.  **Feature Engineering:** `DayOfWeek`, `Month`, and `Day` were extracted from the `Date` column.
+2.  **Label Encoding:** Categorical features (`Category`, `Region`, `Weather`, `Seasonality`) were converted to numerical values.
+3.  **Standard Scaling:** All numerical features were scaled to have a mean of 0 and a standard deviation of 1.
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+#### Training Hyperparameters
 
-[More Information Needed]
+The model was tuned using `RandomizedSearchCV` to find the optimal settings. The best-performing hyperparameters were:
+- **n_estimators:** 200
+- **min_samples_split:** 10
+- **min_samples_leaf:** 4
+- **max_features:** 'sqrt'
+- **max_depth:** 20
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ### Testing Data, Factors & Metrics
 
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+- **Testing Data:** The model was evaluated on a 20% holdout set from the original data, created using a standard `train_test_split` with `random_state=42`.
+- **Metrics:** The primary evaluation metric was **R-squared (R²)** to measure the proportion of variance in sales that the model could predict. **Root Mean Squared Error (RMSE)** and **Mean Absolute Error (MAE)** were also used to measure prediction error in terms of units sold.
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+| Metric         | Value   |
+|----------------|---------|
+| Training R²    | 0.6324  |
+| **Testing R²** | **0.3402** |
+| RMSE           | 88.39   |
+| MAE            | 69.27   |
 
 ## Environmental Impact
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
+- **Hardware Type:** Not tracked (likely trained on Google Colab standard CPU instances).
+- **Hours used:** Not tracked.
+- **Cloud Provider:** Not tracked.
+- **Compute Region:** Not tracked.
+- **Carbon Emitted:** Not tracked.
 
-## Model Card Authors [optional]
+## Model Card Authors
 
-[More Information Needed]
+Hernicksen Satria, Jeremy Wijaya, Lawryan Andrew Darisang (NexusCrew)
 
 ## Model Card Contact
 
-[More Information Needed]
+Please open an issue in the repository for questions or feedback.