Update README.md

README.md

@@ -33,9 +33,9 @@ widget:
     - divsepwid
     - 'single   '
     MORTGAGE:
-    - y
-    - y
-    - n
+    - 'y'
+    - 'y'
+    - 'n'
     NUMCARDS:
     - 2
     - 6
@@ -48,11 +48,14 @@ widget:
     - 3
     - 5
     - 2
+datasets:
+- saifhmb/CreditCardRisk
 ---
 
 # Model description
 
-[More Information Needed]
+This is a logistic regression model trained on customers' credit card risk data in a bank using sklearn library.
+The model predicts whether a customer is worth issuing a credit card or not. The full dataset can be viewed at the following link: https://huggingface.co/datasets/saifhmb/CreditCardRisk
 
 ## Intended uses & limitations
 
@@ -60,7 +63,12 @@ widget:
 
 ## Training Procedure
 
-[More Information Needed]
+The data preprocessing steps applied include the following:
+- Dropping high cardinality features, specifically ID
+- Transforming and Encoding categorical features namely: GENDER, MARITAL, HOWPAID, MORTGAGE and the target variable, RISK
+- Splitting the dataset into training/test set using 85/15 split ratio
+- Applying feature scaling on all features
+
 
 ### Hyperparameters
 
@@ -117,6 +125,9 @@ widget:
 <style>#sk-container-id-16 {color: black;background-color: white;}#sk-container-id-16 pre{padding: 0;}#sk-container-id-16 div.sk-toggleable {background-color: white;}#sk-container-id-16 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-16 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-16 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-16 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-16 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-16 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-16 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-16 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-16 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-16 div.sk-item {position: relative;z-index: 1;}#sk-container-id-16 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-16 div.sk-item::before, #sk-container-id-16 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-16 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-16 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-16 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-16 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-16 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-16 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-16 div.sk-label-container {text-align: center;}#sk-container-id-16 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-16 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-16" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,ColumnTransformer(remainder=&#x27;passthrough&#x27;,transformers=[(&#x27;cat&#x27;,Pipeline(steps=[(&#x27;onehot&#x27;,OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),[&#x27;GENDER&#x27;, &#x27;MARITAL&#x27;,&#x27;HOWPAID&#x27;, &#x27;MORTGAGE&#x27;])])),(&#x27;classifier&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-106" type="checkbox" ><label for="sk-estimator-id-106" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,ColumnTransformer(remainder=&#x27;passthrough&#x27;,transformers=[(&#x27;cat&#x27;,Pipeline(steps=[(&#x27;onehot&#x27;,OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),[&#x27;GENDER&#x27;, &#x27;MARITAL&#x27;,&#x27;HOWPAID&#x27;, &#x27;MORTGAGE&#x27;])])),(&#x27;classifier&#x27;, LogisticRegression())])</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-107" type="checkbox" ><label for="sk-estimator-id-107" class="sk-toggleable__label sk-toggleable__label-arrow">preprocessor: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,transformers=[(&#x27;cat&#x27;,Pipeline(steps=[(&#x27;onehot&#x27;,OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),[&#x27;GENDER&#x27;, &#x27;MARITAL&#x27;, &#x27;HOWPAID&#x27;, &#x27;MORTGAGE&#x27;])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-108" type="checkbox" ><label for="sk-estimator-id-108" class="sk-toggleable__label sk-toggleable__label-arrow">cat</label><div class="sk-toggleable__content"><pre>[&#x27;GENDER&#x27;, &#x27;MARITAL&#x27;, &#x27;HOWPAID&#x27;, &#x27;MORTGAGE&#x27;]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-109" type="checkbox" ><label for="sk-estimator-id-109" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-110" type="checkbox" ><label for="sk-estimator-id-110" class="sk-toggleable__label sk-toggleable__label-arrow">remainder</label><div class="sk-toggleable__content"><pre>[&#x27;AGE&#x27;, &#x27;INCOME&#x27;, &#x27;NUMKIDS&#x27;, &#x27;NUMCARDS&#x27;, &#x27;STORECAR&#x27;, &#x27;LOANS&#x27;]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-111" type="checkbox" ><label for="sk-estimator-id-111" class="sk-toggleable__label sk-toggleable__label-arrow">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-112" type="checkbox" ><label for="sk-estimator-id-112" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div>
 
 ## Evaluation Results
+- The target variable, RISK is multiclass. In sklearn, precision and recall functions have a parameter called,
+average. This parameter is required for a multiclass/multilabel target. average = 'micro' was used to calculate
+the precision and recall metrics globally by counting the total true positives, false negatives and false positives
 
 | Metric    |    Value |
 |-----------|----------|
@@ -134,14 +145,12 @@ widget:
 
 # Model Card Authors
 
-This model card is written by following authors:
+This model card is written by following authors: Seifullah Bello
 
-[More Information Needed]
 
 # Model Card Contact
 
-You can contact the model card authors through following channels:
-[More Information Needed]
+You can contact the model card authors through following channels: [email protected]
 
 # Citation
 
@@ -150,4 +159,4 @@ Below you can find information related to citation.
 **BibTeX:**
 ```
 [More Information Needed]
-```
+```