model_card_added

model_card_template.md

@@ -3,16 +3,52 @@
 For additional information see the Model Card paper: https://arxiv.org/pdf/1810.03993.pdf
 
 ## Model Details
+Model is created by Ashish Kumar Shukla
+
+Model use `RandomForestClassifier` from `sklearn.model.RandomForestClassifier` for classification tasks.
+
+The parameters used are default.
 
 ## Intended Use
+This model predicts whether a person earns over 50k or not based on the census data.
 
 ## Training Data
+More details about the training data: https://archive.ics.uci.edu/ml/datasets/census+income
+
+Extraction was done by Barry Becker from the 1994 Census database.
+
+Prediction task is to determine whether a person makes over 50K a year.
+
+Features:
+ - age: continuous.
+ - workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.
+ - fnlwgt: continuous.
+ - education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.
+ - education-num: continuous.
+ - marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.
+ - occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.
+ - relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.
+ - race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.
+ - sex: Female, Male.
+ - capital-gain: continuous.
+ - capital-loss: continuous.
+ - hours-per-week: continuous.
+ - native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.
+
+For both training and evaluation, categorical features of the data are encoded using `OneHotEncoder` and the target is transformed using `LabelBinarizer`
+
 
 ## Evaluation Data
+The original dataset is first preprocessed and then split into training and evaluation data with evaluation data size of 20%
 
 ## Metrics
-_Please include the metrics used and your model's performance on those metrics._
+Performances of the model:
+- Precision: 0.71 
+- Recall: 0.62 
+- Fbeta: 0.67
 
 ## Ethical Considerations
+This model is trained on census data. The model is not biased towards any particular group of people.
 
 ## Caveats and Recommendations
+I recommend that checks are included upstream of any decision-making points to ensure that bias is minimized.