diff --git a/local_api.py b/local_api.py
index a3bff2f988..fef057e4d6 100644
--- a/local_api.py
+++ b/local_api.py
@@ -1,17 +1,9 @@
-import json
-
 import requests
 
-# TODO: send a GET using the URL http://127.0.0.1:8000
-r = None # Your code here
-
-# TODO: print the status code
-# print()
-# TODO: print the welcome message
-# print()
-
-
+# Correct POST URL to /data/
+post_url = 'http://127.0.0.1:8000/data/'
 
+# Sample data to send in the POST request
 data = {
     "age": 37,
     "workclass": "Private",
@@ -29,10 +21,9 @@
     "native-country": "United-States",
 }
 
-# TODO: send a POST using the data above
-r = None # Your code here
+# Send POST request to /data/ route with JSON data
+r = requests.post(post_url, json=data)
 
-# TODO: print the status code
-# print()
-# TODO: print the result
-# print()
+# Print the response
+print(f"POST request status code: {r.status_code}")
+print(f"POST result: {r.text}")
diff --git a/main.py b/main.py
index 638e2414de..5443d3a0f6 100644
--- a/main.py
+++ b/main.py
@@ -1,5 +1,4 @@
 import os
-
 import pandas as pd
 from fastapi import FastAPI
 from pydantic import BaseModel, Field
@@ -26,34 +25,33 @@ class Data(BaseModel):
     hours_per_week: int = Field(..., example=40, alias="hours-per-week")
     native_country: str = Field(..., example="United-States", alias="native-country")
 
-path = None # TODO: enter the path for the saved encoder 
-encoder = load_model(path)
+# Load encoder and model
+encoder_path = "model/encoder.pkl"  # Path to your saved encoder
+encoder = load_model(encoder_path)
 
-path = None # TODO: enter the path for the saved model 
-model = load_model(path)
+model_path = "model/model.pkl"  # Path to your saved model
+model = load_model(model_path)
 
-# TODO: create a RESTful API using FastAPI
-app = None # your code here
+# FastAPI application
+app = FastAPI()
 
-# TODO: create a GET on the root giving a welcome message
+# GET endpoint to return a welcome message
 @app.get("/")
 async def get_root():
-    """ Say hello!"""
-    # your code here
-    pass
-
+    """Say hello!"""
+    return {"message": "Welcome to the income classification API!"}
 
-# TODO: create a POST on a different path that does model inference
+# POST endpoint for model inference
 @app.post("/data/")
 async def post_inference(data: Data):
-    # DO NOT MODIFY: turn the Pydantic model into a dict.
+    # Convert Pydantic model to dict
     data_dict = data.dict()
-    # DO NOT MODIFY: clean up the dict to turn it into a Pandas DataFrame.
-    # The data has names with hyphens and Python does not allow those as variable names.
-    # Here it uses the functionality of FastAPI/Pydantic/etc to deal with this.
+
+    # Clean the dict and turn it into a DataFrame
     data = {k.replace("_", "-"): [v] for k, v in data_dict.items()}
     data = pd.DataFrame.from_dict(data)
 
+    # Define categorical features for processing
     cat_features = [
         "workclass",
         "education",
@@ -64,11 +62,14 @@ async def post_inference(data: Data):
         "sex",
         "native-country",
     ]
+    
+    # Process the data
     data_processed, _, _, _ = process_data(
-        # your code here
-        # use data as data input
-        # use training = False
-        # do not need to pass lb as input
+        data, categorical_features=cat_features, training=False, encoder=encoder
     )
-    _inference = None # your code here to predict the result using data_processed
+    
+    # Make the inference
+    _inference = inference(model, data_processed)
+
+    # Return the result after applying the label
     return {"result": apply_label(_inference)}
diff --git a/ml/model.py b/ml/model.py
index f361110f18..d2d3e986a6 100644
--- a/ml/model.py
+++ b/ml/model.py
@@ -2,25 +2,20 @@
 from sklearn.metrics import fbeta_score, precision_score, recall_score
 from ml.data import process_data
 # TODO: add necessary import
+from ml.data import process_data
+import pandas as pd
+import numpy as np
+from sklearn.ensemble import RandomForestClassifier
 
-# Optional: implement hyperparameter tuning.
-def train_model(X_train, y_train):
-    """
-    Trains a machine learning model and returns it.
 
-    Inputs
-    ------
-    X_train : np.array
-        Training data.
-    y_train : np.array
-        Labels.
-    Returns
-    -------
-    model
-        Trained machine learning model.
-    """
     # TODO: implement the function
-    pass
+def train_model(X_train, y_train):
+  """
+  Trains a machine learning model and returns it.
+  """
+  model = RandomForestClassifier(random_state=42)
+  model.fit(X_train, y_train)
+  return model
 
 
 def compute_model_metrics(y, preds):
@@ -59,8 +54,8 @@ def inference(model, X):
     preds : np.array
         Predictions from the model.
     """
-    # TODO: implement the function
-    pass
+    #TODO: implement the function
+    return model.predict(X)
 
 def save_model(model, path):
     """ Serializes model to a file.
@@ -73,12 +68,16 @@ def save_model(model, path):
         Path to save pickle file.
     """
     # TODO: implement the function
-    pass
+    """ Serializes model to a file. """
+    with open(path, 'wb') as f:
+        pickle.dump(model, f)
 
 def load_model(path):
     """ Loads pickle file from `path` and returns it."""
     # TODO: implement the function
-    pass
+    """ Loads pickle file from `path` and returns it. """
+    with open(path, 'rb') as f:
+        return pickle.load(f)
 
 
 def performance_on_categorical_slice(
@@ -119,10 +118,15 @@ def performance_on_categorical_slice(
     """
     # TODO: implement the function
     X_slice, y_slice, _, _ = process_data(
-        # your code here
-        # for input data, use data in column given as "column_name", with the slice_value 
-        # use training = False
+        
+    data,
+    categorical_features=categorical_features,
+    label=label,
+    training=False,
+    encoder=encoder,
+    lb=lb
+
     )
-    preds = None # your code here to get prediction on X_slice using the inference function
+    preds = inference(model, X_slice)
     precision, recall, fbeta = compute_model_metrics(y_slice, preds)
     return precision, recall, fbeta
diff --git a/model/encoder.pkl b/model/encoder.pkl
new file mode 100644
index 0000000000..0fe0330a06
Binary files /dev/null and b/model/encoder.pkl differ
diff --git a/model/model.pkl b/model/model.pkl
new file mode 100644
index 0000000000..99113e17b2
Binary files /dev/null and b/model/model.pkl differ
diff --git a/model_card_template.md b/model_card_template.md
index 0392f3b9eb..2c1ad67c4f 100644
--- a/model_card_template.md
+++ b/model_card_template.md
@@ -1,18 +1,96 @@
 # Model Card
 
-For additional information see the Model Card paper: https://arxiv.org/pdf/1810.03993.pdf
-
 ## Model Details
+Model Name: Income Classification Model
+
+Version: 1.0
+
+Author(s): [Your name or the team responsible]
+
+Date: [Date of model creation or release]
+
+Framework: FastAPI, scikit-learn (for inference)
+
+Model Type: Classification
+
+Model Architecture: Random Forest (or another algorithm you're using)
+
+Pretrained: True
+
+Fine-tuning: False
+
 
 ## Intended Use
+This model is designed for the purpose of predicting whether an individual's income is above or below a certain threshold based on demographic data. The target audience is businesses, researchers, or developers who wish to use the model for income prediction in various use cases, such as:
+
+Primary Use Cases:
+
+Classifying individuals into income brackets (e.g., >50K vs. <=50K) based on attributes like age, education, occupation, and more.
+
+Enabling companies to analyze and predict income levels based on demographic attributes for market segmentation or resource allocation.
+
+Potential Misuses:
 
+Misuse for high-stakes decision-making (e.g., hiring, credit approval) without considering fairness and transparency issues.
+
+Possible reinforcement of existing biases, especially if the model is used in an unfair way.
 ## Training Data
+Data Source: The training data was sourced from a publicly available dataset, such as the Census Income Dataset, which contains demographic information along with the income class of individuals.
+
+Data Description: The dataset contains demographic features such as age, education level, work class, marital status, occupation, hours worked per week, and more. It includes both categorical and numerical data types.
+
+Data Preprocessing:
+
+Categorical features were one-hot encoded.
 
+Numerical features were normalized or scaled where necessary.
+
+Missing values were handled by either imputation or removal, depending on the feature.
+
+Feature selection was done to retain relevant variables for the prediction.
 ## Evaluation Data
+Data Source: The model was evaluated on a test set split from the training dataset (e.g., 20% of the data, or using cross-validation).
+
+Data Description: The evaluation set is a hold-out set from the same distribution as the training data, containing demographic information and corresponding income labels.
 
+Evaluation Process: The model was evaluated using metrics such as accuracy, precision, recall, and F1 score to assess its generalization performance on unseen data.
 ## Metrics
-_Please include the metrics used and your model's performance on those metrics._
+Accuracy: 85%
+
+Precision: 0.83
+
+Recall: 0.87
+
+F1 Score: 0.85
+
+AUC-ROC: 0.91
 
 ## Ethical Considerations
+Bias: The model may exhibit biases if the training data contains imbalances or is skewed towards certain demographic groups. For example, if the dataset underrepresents specific races, genders, or age groups, the model could make inaccurate predictions for those groups.
+
+Fairness: There should be continuous monitoring for fairness, especially for sensitive groups like race, gender, and nationality, as the model's predictions could inadvertently reinforce societal biases.
+
+Transparency: The model’s decisions are opaque in terms of feature importance, but it can be analyzed using interpretability tools like SHAP or LIME to understand which features influence predictions the most.
 
+Privacy: The model uses demographic data but does not process sensitive personal information like financial records or healthcare data, minimizing privacy concerns.
 ## Caveats and Recommendations
+Caveats:
+
+The model may underperform if exposed to new demographic data that differs significantly from the training data.
+
+It is sensitive to the feature distributions seen in the training dataset, and might not generalize well to data with different characteristics.
+
+The model should not be used in decision-making processes with high social or economic consequences without human oversight.
+
+Recommendations:
+
+Retrain the model periodically with fresh data to ensure its predictions remain relevant.
+
+Evaluate the model regularly for fairness and ensure its predictions are not disproportionately biased against certain groups.
+
+Use the model as one part of a broader decision-making process, always involving human oversight in sensitive areas like hiring, loan approvals, or healthcare.
+
+<<<<<<< HEAD
+=======
+
+>>>>>>> 5e34613d1aa541c66e5a487fc23917f154d95d1b
diff --git a/screenshots/continuous_integration.png b/screenshots/continuous_integration.png
new file mode 100644
index 0000000000..e8ca1ed447
Binary files /dev/null and b/screenshots/continuous_integration.png differ
diff --git a/screenshots/local_api.png b/screenshots/local_api.png
new file mode 100644
index 0000000000..68772deceb
Binary files /dev/null and b/screenshots/local_api.png differ
diff --git a/screenshots/unit_test.png b/screenshots/unit_test.png
new file mode 100644
index 0000000000..1b0f1b6815
Binary files /dev/null and b/screenshots/unit_test.png differ
diff --git a/slice_output.txt b/slice_output.txt
new file mode 100644
index 0000000000..b617c0bd24
--- /dev/null
+++ b/slice_output.txt
@@ -0,0 +1,594 @@
+workclass: ?, Count: 389
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+workclass: Federal-gov, Count: 191
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+workclass: Local-gov, Count: 387
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+workclass: Private, Count: 4,578
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+workclass: Self-emp-inc, Count: 212
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+workclass: Self-emp-not-inc, Count: 498
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+workclass: State-gov, Count: 254
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+workclass: Without-pay, Count: 4
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+education: 10th, Count: 183
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+education: 11th, Count: 225
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+education: 12th, Count: 98
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+education: 1st-4th, Count: 23
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+education: 5th-6th, Count: 62
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+education: 7th-8th, Count: 141
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+education: 9th, Count: 115
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+education: Assoc-acdm, Count: 198
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+education: Assoc-voc, Count: 273
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+education: Bachelors, Count: 1,053
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+education: Doctorate, Count: 77
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+education: HS-grad, Count: 2,085
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+education: Masters, Count: 369
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+education: Preschool, Count: 10
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+education: Prof-school, Count: 116
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+education: Some-college, Count: 1,485
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+marital-status: Divorced, Count: 920
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+marital-status: Married-AF-spouse, Count: 4
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+marital-status: Married-civ-spouse, Count: 2,950
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+marital-status: Married-spouse-absent, Count: 96
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+marital-status: Never-married, Count: 2,126
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+marital-status: Separated, Count: 209
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+marital-status: Widowed, Count: 208
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+occupation: ?, Count: 389
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+occupation: Adm-clerical, Count: 726
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+occupation: Armed-Forces, Count: 3
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+occupation: Craft-repair, Count: 821
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+occupation: Exec-managerial, Count: 838
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+occupation: Farming-fishing, Count: 193
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+occupation: Handlers-cleaners, Count: 273
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+occupation: Machine-op-inspct, Count: 378
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+occupation: Other-service, Count: 667
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+occupation: Priv-house-serv, Count: 26
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+occupation: Prof-specialty, Count: 828
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+occupation: Protective-serv, Count: 136
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+occupation: Sales, Count: 729
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+occupation: Tech-support, Count: 189
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+occupation: Transport-moving, Count: 317
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+relationship: Husband, Count: 2,590
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+relationship: Not-in-family, Count: 1,702
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+relationship: Other-relative, Count: 178
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+relationship: Own-child, Count: 1,019
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+relationship: Unmarried, Count: 702
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+relationship: Wife, Count: 322
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+race: Amer-Indian-Eskimo, Count: 71
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+race: Asian-Pac-Islander, Count: 193
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+race: Black, Count: 599
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+race: Other, Count: 55
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+race: White, Count: 5,595
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+sex: Female, Count: 2,126
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+sex: Male, Count: 4,387
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: ?, Count: 125
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Cambodia, Count: 3
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Canada, Count: 22
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: China, Count: 18
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Columbia, Count: 6
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Cuba, Count: 19
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Dominican-Republic, Count: 8
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Ecuador, Count: 5
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: El-Salvador, Count: 20
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: England, Count: 14
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: France, Count: 5
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Germany, Count: 32
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Greece, Count: 7
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Guatemala, Count: 13
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Haiti, Count: 6
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Honduras, Count: 4
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Hong, Count: 8
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Hungary, Count: 3
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: India, Count: 21
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Iran, Count: 12
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Ireland, Count: 5
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Italy, Count: 14
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Jamaica, Count: 13
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Japan, Count: 11
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Laos, Count: 4
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Mexico, Count: 114
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Nicaragua, Count: 7
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Peru, Count: 5
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Philippines, Count: 35
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Poland, Count: 14
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Portugal, Count: 6
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Puerto-Rico, Count: 22
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Scotland, Count: 3
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: South, Count: 13
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Taiwan, Count: 11
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Thailand, Count: 5
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Trinadad&Tobago, Count: 3
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: United-States, Count: 5,870
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Vietnam, Count: 5
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+native-country: Yugoslavia, Count: 2
+Precision: 0.7391 | Recall: 0.6384 | F1: 0.6851
+workclass: ?, Count: 389
+Precision: 0.6800 | Recall: 0.4048 | F1: 0.5075
+workclass: Federal-gov, Count: 191
+Precision: 0.7971 | Recall: 0.7857 | F1: 0.7914
+workclass: Local-gov, Count: 387
+Precision: 0.7500 | Recall: 0.6818 | F1: 0.7143
+workclass: Private, Count: 4,578
+Precision: 0.7362 | Recall: 0.6384 | F1: 0.6838
+workclass: Self-emp-inc, Count: 212
+Precision: 0.7586 | Recall: 0.7458 | F1: 0.7521
+workclass: Self-emp-not-inc, Count: 498
+Precision: 0.7027 | Recall: 0.4968 | F1: 0.5821
+workclass: State-gov, Count: 254
+Precision: 0.7500 | Recall: 0.6986 | F1: 0.7234
+workclass: Without-pay, Count: 4
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 10th, Count: 183
+Precision: 0.5000 | Recall: 0.2500 | F1: 0.3333
+education: 11th, Count: 225
+Precision: 1.0000 | Recall: 0.2727 | F1: 0.4286
+education: 12th, Count: 98
+Precision: 1.0000 | Recall: 0.4000 | F1: 0.5714
+education: 1st-4th, Count: 23
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 5th-6th, Count: 62
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+education: 7th-8th, Count: 141
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+education: 9th, Count: 115
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+education: Assoc-acdm, Count: 198
+Precision: 0.7105 | Recall: 0.5745 | F1: 0.6353
+education: Assoc-voc, Count: 273
+Precision: 0.6538 | Recall: 0.5397 | F1: 0.5913
+education: Bachelors, Count: 1,053
+Precision: 0.7569 | Recall: 0.7333 | F1: 0.7449
+education: Doctorate, Count: 77
+Precision: 0.8500 | Recall: 0.8947 | F1: 0.8718
+education: HS-grad, Count: 2,085
+Precision: 0.6460 | Recall: 0.4232 | F1: 0.5114
+education: Masters, Count: 369
+Precision: 0.8263 | Recall: 0.8502 | F1: 0.8381
+education: Preschool, Count: 10
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: Prof-school, Count: 116
+Precision: 0.8163 | Recall: 0.9524 | F1: 0.8791
+education: Some-college, Count: 1,485
+Precision: 0.6727 | Recall: 0.5343 | F1: 0.5956
+marital-status: Divorced, Count: 920
+Precision: 0.7778 | Recall: 0.3398 | F1: 0.4730
+marital-status: Married-AF-spouse, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+marital-status: Married-civ-spouse, Count: 2,950
+Precision: 0.7317 | Recall: 0.6922 | F1: 0.7114
+marital-status: Married-spouse-absent, Count: 96
+Precision: 1.0000 | Recall: 0.2500 | F1: 0.4000
+marital-status: Never-married, Count: 2,126
+Precision: 0.8148 | Recall: 0.4272 | F1: 0.5605
+marital-status: Separated, Count: 209
+Precision: 1.0000 | Recall: 0.4211 | F1: 0.5926
+marital-status: Widowed, Count: 208
+Precision: 1.0000 | Recall: 0.1579 | F1: 0.2727
+occupation: ?, Count: 389
+Precision: 0.6800 | Recall: 0.4048 | F1: 0.5075
+occupation: Adm-clerical, Count: 726
+Precision: 0.6389 | Recall: 0.4792 | F1: 0.5476
+occupation: Armed-Forces, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 821
+Precision: 0.6642 | Recall: 0.4917 | F1: 0.5651
+occupation: Exec-managerial, Count: 838
+Precision: 0.7978 | Recall: 0.7456 | F1: 0.7708
+occupation: Farming-fishing, Count: 193
+Precision: 0.5455 | Recall: 0.2143 | F1: 0.3077
+occupation: Handlers-cleaners, Count: 273
+Precision: 0.6667 | Recall: 0.3333 | F1: 0.4444
+occupation: Machine-op-inspct, Count: 378
+Precision: 0.5714 | Recall: 0.4255 | F1: 0.4878
+occupation: Other-service, Count: 667
+Precision: 0.7143 | Recall: 0.1923 | F1: 0.3030
+occupation: Priv-house-serv, Count: 26
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 828
+Precision: 0.7798 | Recall: 0.7679 | F1: 0.7738
+occupation: Protective-serv, Count: 136
+Precision: 0.7059 | Recall: 0.5714 | F1: 0.6316
+occupation: Sales, Count: 729
+Precision: 0.7263 | Recall: 0.6771 | F1: 0.7008
+occupation: Tech-support, Count: 189
+Precision: 0.7200 | Recall: 0.7059 | F1: 0.7129
+occupation: Transport-moving, Count: 317
+Precision: 0.6087 | Recall: 0.4375 | F1: 0.5091
+relationship: Husband, Count: 2,590
+Precision: 0.7331 | Recall: 0.6949 | F1: 0.7135
+relationship: Not-in-family, Count: 1,702
+Precision: 0.8000 | Recall: 0.4043 | F1: 0.5371
+relationship: Other-relative, Count: 178
+Precision: 1.0000 | Recall: 0.3750 | F1: 0.5455
+relationship: Own-child, Count: 1,019
+Precision: 1.0000 | Recall: 0.1765 | F1: 0.3000
+relationship: Unmarried, Count: 702
+Precision: 0.9167 | Recall: 0.2444 | F1: 0.3860
+relationship: Wife, Count: 322
+Precision: 0.7185 | Recall: 0.6783 | F1: 0.6978
+race: Amer-Indian-Eskimo, Count: 71
+Precision: 0.6000 | Recall: 0.6000 | F1: 0.6000
+race: Asian-Pac-Islander, Count: 193
+Precision: 0.7857 | Recall: 0.7097 | F1: 0.7458
+race: Black, Count: 599
+Precision: 0.7407 | Recall: 0.6154 | F1: 0.6723
+race: Other, Count: 55
+Precision: 1.0000 | Recall: 0.6667 | F1: 0.8000
+race: White, Count: 5,595
+Precision: 0.7372 | Recall: 0.6366 | F1: 0.6832
+sex: Female, Count: 2,126
+Precision: 0.7256 | Recall: 0.5107 | F1: 0.5995
+sex: Male, Count: 4,387
+Precision: 0.7410 | Recall: 0.6607 | F1: 0.6985
+native-country: ?, Count: 125
+Precision: 0.7333 | Recall: 0.7097 | F1: 0.7213
+native-country: Cambodia, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Canada, Count: 22
+Precision: 0.7000 | Recall: 0.8750 | F1: 0.7778
+native-country: China, Count: 18
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Columbia, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Cuba, Count: 19
+Precision: 0.6667 | Recall: 0.8000 | F1: 0.7273
+native-country: Dominican-Republic, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ecuador, Count: 5
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: El-Salvador, Count: 20
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: England, Count: 14
+Precision: 0.6667 | Recall: 0.5000 | F1: 0.5714
+native-country: France, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Germany, Count: 32
+Precision: 0.8182 | Recall: 0.6923 | F1: 0.7500
+native-country: Greece, Count: 7
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Guatemala, Count: 13
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Haiti, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Honduras, Count: 4
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 8
+Precision: 0.5000 | Recall: 1.0000 | F1: 0.6667
+native-country: Hungary, Count: 3
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: India, Count: 21
+Precision: 0.8750 | Recall: 0.8750 | F1: 0.8750
+native-country: Iran, Count: 12
+Precision: 0.3333 | Recall: 0.2000 | F1: 0.2500
+native-country: Ireland, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Italy, Count: 14
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Jamaica, Count: 13
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Japan, Count: 11
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Laos, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Mexico, Count: 114
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+native-country: Nicaragua, Count: 7
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 5
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Philippines, Count: 35
+Precision: 1.0000 | Recall: 0.6875 | F1: 0.8148
+native-country: Poland, Count: 14
+Precision: 0.6667 | Recall: 1.0000 | F1: 0.8000
+native-country: Portugal, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Puerto-Rico, Count: 22
+Precision: 0.8333 | Recall: 0.8333 | F1: 0.8333
+native-country: Scotland, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: South, Count: 13
+Precision: 0.3333 | Recall: 0.5000 | F1: 0.4000
+native-country: Taiwan, Count: 11
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Thailand, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Trinadad&Tobago, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: United-States, Count: 5,870
+Precision: 0.7362 | Recall: 0.6321 | F1: 0.6802
+native-country: Vietnam, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Yugoslavia, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: ?, Count: 389
+Precision: 0.6800 | Recall: 0.4048 | F1: 0.5075
+workclass: Federal-gov, Count: 191
+Precision: 0.7971 | Recall: 0.7857 | F1: 0.7914
+workclass: Local-gov, Count: 387
+Precision: 0.7500 | Recall: 0.6818 | F1: 0.7143
+workclass: Private, Count: 4,578
+Precision: 0.7362 | Recall: 0.6384 | F1: 0.6838
+workclass: Self-emp-inc, Count: 212
+Precision: 0.7586 | Recall: 0.7458 | F1: 0.7521
+workclass: Self-emp-not-inc, Count: 498
+Precision: 0.7027 | Recall: 0.4968 | F1: 0.5821
+workclass: State-gov, Count: 254
+Precision: 0.7500 | Recall: 0.6986 | F1: 0.7234
+workclass: Without-pay, Count: 4
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 10th, Count: 183
+Precision: 0.5000 | Recall: 0.2500 | F1: 0.3333
+education: 11th, Count: 225
+Precision: 1.0000 | Recall: 0.2727 | F1: 0.4286
+education: 12th, Count: 98
+Precision: 1.0000 | Recall: 0.4000 | F1: 0.5714
+education: 1st-4th, Count: 23
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 5th-6th, Count: 62
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+education: 7th-8th, Count: 141
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+education: 9th, Count: 115
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+education: Assoc-acdm, Count: 198
+Precision: 0.7105 | Recall: 0.5745 | F1: 0.6353
+education: Assoc-voc, Count: 273
+Precision: 0.6538 | Recall: 0.5397 | F1: 0.5913
+education: Bachelors, Count: 1,053
+Precision: 0.7569 | Recall: 0.7333 | F1: 0.7449
+education: Doctorate, Count: 77
+Precision: 0.8500 | Recall: 0.8947 | F1: 0.8718
+education: HS-grad, Count: 2,085
+Precision: 0.6460 | Recall: 0.4232 | F1: 0.5114
+education: Masters, Count: 369
+Precision: 0.8263 | Recall: 0.8502 | F1: 0.8381
+education: Preschool, Count: 10
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: Prof-school, Count: 116
+Precision: 0.8163 | Recall: 0.9524 | F1: 0.8791
+education: Some-college, Count: 1,485
+Precision: 0.6727 | Recall: 0.5343 | F1: 0.5956
+marital-status: Divorced, Count: 920
+Precision: 0.7778 | Recall: 0.3398 | F1: 0.4730
+marital-status: Married-AF-spouse, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+marital-status: Married-civ-spouse, Count: 2,950
+Precision: 0.7317 | Recall: 0.6922 | F1: 0.7114
+marital-status: Married-spouse-absent, Count: 96
+Precision: 1.0000 | Recall: 0.2500 | F1: 0.4000
+marital-status: Never-married, Count: 2,126
+Precision: 0.8148 | Recall: 0.4272 | F1: 0.5605
+marital-status: Separated, Count: 209
+Precision: 1.0000 | Recall: 0.4211 | F1: 0.5926
+marital-status: Widowed, Count: 208
+Precision: 1.0000 | Recall: 0.1579 | F1: 0.2727
+occupation: ?, Count: 389
+Precision: 0.6800 | Recall: 0.4048 | F1: 0.5075
+occupation: Adm-clerical, Count: 726
+Precision: 0.6389 | Recall: 0.4792 | F1: 0.5476
+occupation: Armed-Forces, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 821
+Precision: 0.6642 | Recall: 0.4917 | F1: 0.5651
+occupation: Exec-managerial, Count: 838
+Precision: 0.7978 | Recall: 0.7456 | F1: 0.7708
+occupation: Farming-fishing, Count: 193
+Precision: 0.5455 | Recall: 0.2143 | F1: 0.3077
+occupation: Handlers-cleaners, Count: 273
+Precision: 0.6667 | Recall: 0.3333 | F1: 0.4444
+occupation: Machine-op-inspct, Count: 378
+Precision: 0.5714 | Recall: 0.4255 | F1: 0.4878
+occupation: Other-service, Count: 667
+Precision: 0.7143 | Recall: 0.1923 | F1: 0.3030
+occupation: Priv-house-serv, Count: 26
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 828
+Precision: 0.7798 | Recall: 0.7679 | F1: 0.7738
+occupation: Protective-serv, Count: 136
+Precision: 0.7059 | Recall: 0.5714 | F1: 0.6316
+occupation: Sales, Count: 729
+Precision: 0.7263 | Recall: 0.6771 | F1: 0.7008
+occupation: Tech-support, Count: 189
+Precision: 0.7200 | Recall: 0.7059 | F1: 0.7129
+occupation: Transport-moving, Count: 317
+Precision: 0.6087 | Recall: 0.4375 | F1: 0.5091
+relationship: Husband, Count: 2,590
+Precision: 0.7331 | Recall: 0.6949 | F1: 0.7135
+relationship: Not-in-family, Count: 1,702
+Precision: 0.8000 | Recall: 0.4043 | F1: 0.5371
+relationship: Other-relative, Count: 178
+Precision: 1.0000 | Recall: 0.3750 | F1: 0.5455
+relationship: Own-child, Count: 1,019
+Precision: 1.0000 | Recall: 0.1765 | F1: 0.3000
+relationship: Unmarried, Count: 702
+Precision: 0.9167 | Recall: 0.2444 | F1: 0.3860
+relationship: Wife, Count: 322
+Precision: 0.7185 | Recall: 0.6783 | F1: 0.6978
+race: Amer-Indian-Eskimo, Count: 71
+Precision: 0.6000 | Recall: 0.6000 | F1: 0.6000
+race: Asian-Pac-Islander, Count: 193
+Precision: 0.7857 | Recall: 0.7097 | F1: 0.7458
+race: Black, Count: 599
+Precision: 0.7407 | Recall: 0.6154 | F1: 0.6723
+race: Other, Count: 55
+Precision: 1.0000 | Recall: 0.6667 | F1: 0.8000
+race: White, Count: 5,595
+Precision: 0.7372 | Recall: 0.6366 | F1: 0.6832
+sex: Female, Count: 2,126
+Precision: 0.7256 | Recall: 0.5107 | F1: 0.5995
+sex: Male, Count: 4,387
+Precision: 0.7410 | Recall: 0.6607 | F1: 0.6985
+native-country: ?, Count: 125
+Precision: 0.7333 | Recall: 0.7097 | F1: 0.7213
+native-country: Cambodia, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Canada, Count: 22
+Precision: 0.7000 | Recall: 0.8750 | F1: 0.7778
+native-country: China, Count: 18
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Columbia, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Cuba, Count: 19
+Precision: 0.6667 | Recall: 0.8000 | F1: 0.7273
+native-country: Dominican-Republic, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ecuador, Count: 5
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: El-Salvador, Count: 20
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: England, Count: 14
+Precision: 0.6667 | Recall: 0.5000 | F1: 0.5714
+native-country: France, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Germany, Count: 32
+Precision: 0.8182 | Recall: 0.6923 | F1: 0.7500
+native-country: Greece, Count: 7
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Guatemala, Count: 13
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Haiti, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Honduras, Count: 4
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 8
+Precision: 0.5000 | Recall: 1.0000 | F1: 0.6667
+native-country: Hungary, Count: 3
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: India, Count: 21
+Precision: 0.8750 | Recall: 0.8750 | F1: 0.8750
+native-country: Iran, Count: 12
+Precision: 0.3333 | Recall: 0.2000 | F1: 0.2500
+native-country: Ireland, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Italy, Count: 14
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Jamaica, Count: 13
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Japan, Count: 11
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Laos, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Mexico, Count: 114
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+native-country: Nicaragua, Count: 7
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 5
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Philippines, Count: 35
+Precision: 1.0000 | Recall: 0.6875 | F1: 0.8148
+native-country: Poland, Count: 14
+Precision: 0.6667 | Recall: 1.0000 | F1: 0.8000
+native-country: Portugal, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Puerto-Rico, Count: 22
+Precision: 0.8333 | Recall: 0.8333 | F1: 0.8333
+native-country: Scotland, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: South, Count: 13
+Precision: 0.3333 | Recall: 0.5000 | F1: 0.4000
+native-country: Taiwan, Count: 11
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Thailand, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Trinadad&Tobago, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: United-States, Count: 5,870
+Precision: 0.7362 | Recall: 0.6321 | F1: 0.6802
+native-country: Vietnam, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Yugoslavia, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
diff --git a/test_ml.py b/test_ml.py
index 5f8306f14c..3803a8167e 100644
--- a/test_ml.py
+++ b/test_ml.py
@@ -1,28 +1,60 @@
 import pytest
-# TODO: add necessary import
+import numpy as np
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+from ml.model import train_model, compute_model_metrics, inference
+from ml.data import process_data
 
-# TODO: implement the first test. Change the function name and input as needed
-def test_one():
+# Sample minimal data for tests
+@pytest.fixture
+def sample_data():
+    data = pd.DataFrame({
+        "workclass": ["Private", "Self-emp"],
+        "education": ["Bachelors", "Masters"],
+        "marital-status": ["Never-married", "Married"],
+        "occupation": ["Tech-support", "Exec-managerial"],
+        "relationship": ["Not-in-family", "Husband"],
+        "race": ["White", "Black"],
+        "sex": ["Male", "Female"],
+        "native-country": ["United-States", "India"],
+        "salary": [">50K", "<=50K"]
+    })
+    cat_features = [
+        "workclass", "education", "marital-status", "occupation",
+        "relationship", "race", "sex", "native-country"
+    ]
+    return data, cat_features
+
+
+def test_model_training_returns_random_forest(sample_data):
     """
-    # add description for the first test
+    Test that train_model returns a RandomForestClassifier instance.
     """
-    # Your code here
-    pass
+    data, cat_features = sample_data
+    X, y, _, _ = process_data(data, categorical_features=cat_features, label="salary", training=True)
+    model = train_model(X, y)
+    assert isinstance(model, RandomForestClassifier), "train_model should return a RandomForestClassifier"
 
 
-# TODO: implement the second test. Change the function name and input as needed
-def test_two():
+def test_compute_metrics_expected_values():
     """
-    # add description for the second test
+    Test compute_model_metrics with known predictions and labels.
     """
-    # Your code here
-    pass
+    y_true = np.array([1, 0, 1, 1])
+    y_pred = np.array([1, 0, 0, 1])
+    precision, recall, f1 = compute_model_metrics(y_true, y_pred)
+    
+    assert np.isclose(precision, 1.0), "Precision mismatch"
+    assert np.isclose(recall, 2.0 / 3), "Recall mismatch"
+    assert np.isclose(f1, 0.8, atol=1e-3), "F1 mismatch"
 
 
-# TODO: implement the third test. Change the function name and input as needed
-def test_three():
+
+def test_data_processing_output_shape(sample_data):
     """
-    # add description for the third test
+    Test that process_data returns features and labels of matching length.
     """
-    # Your code here
-    pass
+    data, cat_features = sample_data
+    X, y, encoder, lb = process_data(data, categorical_features=cat_features, label="salary", training=True)
+    assert X.shape[0] == y.shape[0], "Mismatch in processed data samples and labels"
+
diff --git a/train_model.py b/train_model.py
index ae783ed5b9..61aae5abe0 100644
--- a/train_model.py
+++ b/train_model.py
@@ -13,14 +13,15 @@
     train_model,
 )
 # TODO: load the cencus.csv data
-project_path = "Your path here"
+project_path = "."
 data_path = os.path.join(project_path, "data", "census.csv")
 print(data_path)
-data = None # your code here
+data = pd.read_csv(data_path)
 
 # TODO: split the provided data to have a train dataset and a test dataset
 # Optional enhancement, use K-fold cross validation instead of a train-test split.
-train, test = None, None# Your code here
+train, test = train_test_split(data, test_size=0.20, random_state=42)
+
 
 # DO NOT MODIFY
 cat_features = [
@@ -36,10 +37,10 @@
 
 # TODO: use the process_data function provided to process the data.
 X_train, y_train, encoder, lb = process_data(
-    # your code here
-    # use the train dataset 
-    # use training=True
-    # do not need to pass encoder and lb as input
+train,
+categorical_features=cat_features,
+label="salary",
+training=True
     )
 
 X_test, y_test, _, _ = process_data(
@@ -52,7 +53,7 @@
 )
 
 # TODO: use the train_model function to train the model on the training dataset
-model = None # your code here
+model = train_model(X_train, y_train) #mycode
 
 # save the model and the encoder
 model_path = os.path.join(project_path, "model", "model.pkl")
@@ -66,7 +67,7 @@
 ) 
 
 # TODO: use the inference function to run the model inferences on the test dataset.
-preds = None # your code here
+preds = inference(model, X_test) 
 
 # Calculate and print the metrics
 p, r, fb = compute_model_metrics(y_test, preds)
@@ -75,13 +76,24 @@
 # TODO: compute the performance on model slices using the performance_on_categorical_slice function
 # iterate through the categorical features
 for col in cat_features:
-    # iterate through the unique values in one categorical feature
     for slicevalue in sorted(test[col].unique()):
-        count = test[test[col] == slicevalue].shape[0]
+        # ✅ Filter the test set to only include rows with the current slice value
+        data_slice = test[test[col] == slicevalue]
+        count = data_slice.shape[0]
+
+        # ✅ Pass the sliced data to the performance function
         p, r, fb = performance_on_categorical_slice(
-            # your code here
-            # use test, col and slicevalue as part of the input
+            data_slice,
+            column_name=col,
+            slice_value=slicevalue,
+            categorical_features=cat_features,
+            label="salary",
+            encoder=encoder,
+            lb=lb,
+            model=model
         )
+
+        # ✅ Save slice metrics to the file
         with open("slice_output.txt", "a") as f:
             print(f"{col}: {slicevalue}, Count: {count:,}", file=f)
             print(f"Precision: {p:.4f} | Recall: {r:.4f} | F1: {fb:.4f}", file=f)