diff --git a/local_api.py b/local_api.py
index a3bff2f988..275f4daec3 100644
--- a/local_api.py
+++ b/local_api.py
@@ -2,17 +2,18 @@
 
 import requests
 
-# TODO: send a GET using the URL http://127.0.0.1:8000
-r = None # Your code here
+# Send a GET using the URL http://127.0.0.1:8000
+url="http://127.0.0.1:8000/predict" # Your code here
 
+r = requests.get("http://127.0.0.1:8000")
 # TODO: print the status code
-# print()
+print("Status Code:", r.status_code)
 # TODO: print the welcome message
-# print()
+print("Results:", r.json())
 
 
 
-data = {
+input_data = {
     "age": 37,
     "workclass": "Private",
     "fnlgt": 178356,
@@ -26,13 +27,19 @@
     "capital-gain": 0,
     "capital-loss": 0,
     "hours-per-week": 40,
-    "native-country": "United-States",
+    "native-country": "United-States"
 }
 
-# TODO: send a POST using the data above
-r = None # Your code here
 
-# TODO: print the status code
-# print()
-# TODO: print the result
-# print()
+# Send a POST using the data above
+response = requests.post(url, json=input_data) # Your code here
+
+# Print the status code
+print("Status Code: {response.status_code}")
+
+print("Response text:", response.text)
+# Print the result
+if response.status_code == 200:
+    print("Result:", response.json())
+else:
+    print(f"Failed with status code {response.status_code}. Response text: {response.text}")
diff --git a/main.py b/main.py
index 638e2414de..b52918becc 100644
--- a/main.py
+++ b/main.py
@@ -1,8 +1,9 @@
 import os
-
+import pickle
 import pandas as pd
-from fastapi import FastAPI
+from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel, Field
+import joblib
 
 from ml.data import apply_label, process_data
 from ml.model import inference, load_model
@@ -25,26 +26,33 @@ class Data(BaseModel):
     capital_loss: int = Field(..., example=0, alias="capital-loss")
     hours_per_week: int = Field(..., example=40, alias="hours-per-week")
     native_country: str = Field(..., example="United-States", alias="native-country")
+try:
+    encoder_path = os.path.join(os.getcwd(), "model", "encoder.pkl") # TODO: enter the path for the saved encoder 
+    encoder = load_model(encoder_path)
+    print("Encoder loaded successfully.")
+
+    model_path = os.path.join(os.getcwd(), "model","model.pkl") # TODO: enter the path for the saved model 
+    model = load_model(model_path)
+    print("Model loaded successfully.")
 
-path = None # TODO: enter the path for the saved encoder 
-encoder = load_model(path)
 
-path = None # TODO: enter the path for the saved model 
-model = load_model(path)
+except Exception as e:
+    print(f"Error loading model, encoder, or label binarizer: {e}")
+    raise HTTPException(status_code=500,  detail=f"Error loading model, encoder, or label binarizer: {str(e)}")
 
-# TODO: create a RESTful API using FastAPI
-app = None # your code here
+# Create a RESTful API using FastAPI
+app = FastAPI() # your code here
 
-# TODO: create a GET on the root giving a welcome message
+# Create a GET on the root giving a welcome message
 @app.get("/")
 async def get_root():
     """ Say hello!"""
     # your code here
-    pass
+    return {"message": "Hello from the API!"}
 
 
-# TODO: create a POST on a different path that does model inference
-@app.post("/data/")
+# Create a POST on a different path that does model inference
+@app.post("/predict")
 async def post_inference(data: Data):
     # DO NOT MODIFY: turn the Pydantic model into a dict.
     data_dict = data.dict()
@@ -65,10 +73,14 @@ async def post_inference(data: Data):
         "native-country",
     ]
     data_processed, _, _, _ = process_data(
-        # your code here
+        data,
+        categorical_features=cat_features,
+        training=False,# your code here
+        encoder=encoder
         # use data as data input
         # use training = False
         # do not need to pass lb as input
     )
-    _inference = None # your code here to predict the result using data_processed
-    return {"result": apply_label(_inference)}
+    _inference = model.predict(data_processed)
+    result = apply_label(_inference)
+    return {"result": result}
diff --git a/ml/data.py b/ml/data.py
index f8d30b5b16..73dc292af1 100644
--- a/ml/data.py
+++ b/ml/data.py
@@ -53,12 +53,15 @@ def process_data(
     X_categorical = X[categorical_features].values
     X_continuous = X.drop(*[categorical_features], axis=1)
 
-    if training is True:
+    if training:
         encoder = OneHotEncoder(sparse_output=False, handle_unknown="ignore")
         lb = LabelBinarizer()
         X_categorical = encoder.fit_transform(X_categorical)
         y = lb.fit_transform(y.values).ravel()
     else:
+        if encoder is None:
+            raise ValueError("Encoder and LabelBinarizer must be provided during inference")
+
         X_categorical = encoder.transform(X_categorical)
         try:
             y = lb.transform(y.values).ravel()
diff --git a/ml/model.py b/ml/model.py
index f361110f18..f9831a08af 100644
--- a/ml/model.py
+++ b/ml/model.py
@@ -2,9 +2,12 @@
 from sklearn.metrics import fbeta_score, precision_score, recall_score
 from ml.data import process_data
 # TODO: add necessary import
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import GridSearchCV, StratifiedKFold
+import joblib
 
 # Optional: implement hyperparameter tuning.
-def train_model(X_train, y_train):
+def train_model(X_train, y_train, cv=None):
     """
     Trains a machine learning model and returns it.
 
@@ -19,8 +22,19 @@ def train_model(X_train, y_train):
     model
         Trained machine learning model.
     """
-    # TODO: implement the function
-    pass
+    # Train and return a model
+    if cv is None:
+        cv = StratifiedKFold(n_splits=5)
+    
+    param_grid = {
+        'n_estimators': [100, 200],
+        'max_depth': [None, 10, 20],
+    }
+    clf = GridSearchCV(RandomForestClassifier(random_state=42), param_grid, cv=cv)
+    clf.fit(X_train, y_train)
+    return clf.best_estimator_
+
+
 
 
 def compute_model_metrics(y, preds):
@@ -50,7 +64,7 @@ def inference(model, X):
 
     Inputs
     ------
-    model : ???
+    model : sklearn.base.BaseEstimator
         Trained machine learning model.
     X : np.array
         Data used for prediction.
@@ -59,8 +73,8 @@ def inference(model, X):
     preds : np.array
         Predictions from the model.
     """
-    # TODO: implement the function
-    pass
+    # Run model inferences and return the predictions
+    return model.predict(X)
 
 def save_model(model, path):
     """ Serializes model to a file.
@@ -72,13 +86,13 @@ def save_model(model, path):
     path : str
         Path to save pickle file.
     """
-    # TODO: implement the function
-    pass
+    # Save a model
+    joblib.dump(model, path)
 
 def load_model(path):
     """ Loads pickle file from `path` and returns it."""
-    # TODO: implement the function
-    pass
+    # Load a model
+    return joblib.load(path)
 
 
 def performance_on_categorical_slice(
@@ -107,7 +121,7 @@ def performance_on_categorical_slice(
         Trained sklearn OneHotEncoder, only used if training=False.
     lb : sklearn.preprocessing._label.LabelBinarizer
         Trained sklearn LabelBinarizer, only used if training=False.
-    model : ???
+    model : RandomForestClassifier
         Model used for the task.
 
     Returns
@@ -117,12 +131,33 @@ def performance_on_categorical_slice(
     fbeta : float
 
     """
-    # TODO: implement the function
+    # Computes the metrics on a slice of the data
+    data_slice = data[data[column_name]==slice_value]
+    
     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_slice,
+        categorical_features=categorical_features,
+        label=label,
+        encoder=encoder,
+        lb=lb,
+        training=False
     )
-    preds = None # your code here to get prediction on X_slice using the inference function
+    preds = inference(model, X_slice) # your code here to get prediction on X_slice using the inference function
     precision, recall, fbeta = compute_model_metrics(y_slice, preds)
+
+    #Prepare the log message for this slice
+    log_message = (
+        f"Precision: {precision:.4f} | Recall: {recall:.4f} | F1: {fbeta:.4f}\n"
+        f"{column_name}: {slice_value}, Count: {len(data_slice)}\n"
+    )
+    #Apend the results to slice_output.txt
+    with open('slice_output.txt', 'a') as f:
+        f.write(log_message)
+    
+    #Print the result for terminal log
+    print(log_message)
+
     return precision, recall, fbeta
diff --git a/model/encoder.pkl b/model/encoder.pkl
new file mode 100644
index 0000000000..44a41b95e8
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..03364b3ac3
Binary files /dev/null and b/model/model.pkl differ
diff --git a/model_card_template.md b/model_card_template.md
index 0392f3b9eb..7705b94a3a 100644
--- a/model_card_template.md
+++ b/model_card_template.md
@@ -3,16 +3,53 @@
 For additional information see the Model Card paper: https://arxiv.org/pdf/1810.03993.pdf
 
 ## Model Details
-
+Model Name: Random Forest Classifier
+Version: v1.0
+Type: Supervised Classification (Binary Classification)
+Model Architecture: Random Forest with hyperparameter tuning (n_estimators: 100-200, max_depth: None, 10, 20) using GridSearchCV
+Training Time: Approximately 2 hours (estimated)
+Last Trained: 2025-05-04
 ## Intended Use
+The model is designed to predict whether an individual's income exceeds $50k per year, based on demographic and employment-related features from U.S. Census data. Intended for educational purposes, especially for demonstrating machine learning workflows and deployment pipelines. 
 
 ## Training Data
+Source: UC Irvine Adult Census Income Dataset (loaded from census.csv)
+Features:
+    -Categorical: workclass, education, marital-status, occupation, relationship, race, sex, native-country
+    -Numerical: age, hours-per-week, education-num, capital-gain, capital-loss, etc
+Target Label: salary (<=50k, >50k)
+Preprocessing:
+    -OneHotEncoding for categorical variables
+    -LabelBinarizer for the target variable
+    -Train-test split: 80/20
 
 ## Evaluation Data
+The test dataset is a 20% hold-out sample from the original dataset
+Used to evaluate generalization performance and perform slice-based fairness analysis
 
 ## Metrics
 _Please include the metrics used and your model's performance on those metrics._
+Precision: 0.7866
+Recall: 0.6149
+F1 Score: 0.6902
+
+These results suggest that the model strikes a moderate balance between identifying positive cases (recall) and minimizing false positives (precision)
+
+See slice_output.txt
 
 ## Ethical Considerations
+Bias & Fairness: The model may reflect historical biases present in the U.S. Census dataset. For instance, features such as race and gender could lead to disparate performance on different groups.
+
+Interpretability: Random Forests are not easily interpretable by default, which may limit their use in regulated environments.
+
+Use Limitations: This model should not be used for automated income predicitions affecting real-world decisions without additional audits and mitigation techniques.
+
+Data Privacy: The dataset does not contain personally identifiable information (PII), but proper care should be taken if extended to real-world data.
 
 ## Caveats and Recommendations
+Performance may vary significantly across different population subgroups. Always check model performance using slice-based analysis.
+Further model robustness checks are recommended before deployment.
+For production use, consider model interpretability enhancements such as SHAP or LIME for local explanations. 
+LIME https://lime-ml.readthedocs.io/en/latest/
+SHAP https://shap.readthedocs.io/en/latest/ 
+
diff --git a/screenshots/local_api.png b/screenshots/local_api.png
new file mode 100644
index 0000000000..5c347b6fc8
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..f604fbeb6c
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..f4d733f71e
--- /dev/null
+++ b/slice_output.txt
@@ -0,0 +1,596 @@
+workclass: ?, Count: 389
+Precision: 0.7391 | Recall: 0.4048 | F1: 0.5231
+workclass: Federal-gov, Count: 191
+Precision: 0.7846 | Recall: 0.7286 | F1: 0.7556
+workclass: Local-gov, Count: 387
+Precision: 0.7624 | Recall: 0.7000 | F1: 0.7299
+workclass: Private, Count: 4,578
+Precision: 0.8035 | Recall: 0.6004 | F1: 0.6872
+workclass: Self-emp-inc, Count: 212
+Precision: 0.7815 | Recall: 0.7881 | F1: 0.7848
+workclass: Self-emp-not-inc, Count: 498
+Precision: 0.7451 | Recall: 0.4841 | F1: 0.5869
+workclass: State-gov, Count: 254
+Precision: 0.7286 | Recall: 0.6986 | F1: 0.7133
+workclass: Without-pay, Count: 4
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 10th, Count: 183
+Precision: 1.0000 | Recall: 0.0833 | F1: 0.1538
+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: 0.7568 | Recall: 0.5957 | F1: 0.6667
+education: Assoc-voc, Count: 273
+Precision: 0.7209 | Recall: 0.4921 | F1: 0.5849
+education: Bachelors, Count: 1,053
+Precision: 0.7547 | Recall: 0.8000 | F1: 0.7767
+education: Doctorate, Count: 77
+Precision: 0.8387 | Recall: 0.9123 | F1: 0.8739
+education: HS-grad, Count: 2,085
+Precision: 0.8534 | Recall: 0.2870 | F1: 0.4295
+education: Masters, Count: 369
+Precision: 0.8219 | Recall: 0.8696 | F1: 0.8451
+education: Preschool, Count: 10
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: Prof-school, Count: 116
+Precision: 0.8211 | Recall: 0.9286 | F1: 0.8715
+education: Some-college, Count: 1,485
+Precision: 0.7588 | Recall: 0.4657 | F1: 0.5772
+marital-status: Divorced, Count: 920
+Precision: 0.8537 | Recall: 0.3398 | F1: 0.4861
+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: 0.7778 | Recall: 0.6702 | F1: 0.7200
+marital-status: Married-spouse-absent, Count: 96
+Precision: 0.6667 | Recall: 0.2500 | F1: 0.3636
+marital-status: Never-married, Count: 2,126
+Precision: 0.9231 | Recall: 0.3495 | F1: 0.5070
+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: 0.7391 | Recall: 0.4048 | F1: 0.5231
+occupation: Adm-clerical, Count: 726
+Precision: 0.6825 | Recall: 0.4479 | F1: 0.5409
+occupation: Armed-Forces, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 821
+Precision: 0.8500 | Recall: 0.3757 | F1: 0.5211
+occupation: Exec-managerial, Count: 838
+Precision: 0.8147 | Recall: 0.7531 | F1: 0.7827
+occupation: Farming-fishing, Count: 193
+Precision: 0.6667 | Recall: 0.2143 | F1: 0.3243
+occupation: Handlers-cleaners, Count: 273
+Precision: 0.5714 | Recall: 0.3333 | F1: 0.4211
+occupation: Machine-op-inspct, Count: 378
+Precision: 0.7647 | Recall: 0.2766 | F1: 0.4062
+occupation: Other-service, Count: 667
+Precision: 0.8333 | Recall: 0.1923 | F1: 0.3125
+occupation: Priv-house-serv, Count: 26
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 828
+Precision: 0.7920 | Recall: 0.8061 | F1: 0.7990
+occupation: Protective-serv, Count: 136
+Precision: 0.7200 | Recall: 0.4286 | F1: 0.5373
+occupation: Sales, Count: 729
+Precision: 0.7607 | Recall: 0.6458 | F1: 0.6986
+occupation: Tech-support, Count: 189
+Precision: 0.7273 | Recall: 0.6275 | F1: 0.6737
+occupation: Transport-moving, Count: 317
+Precision: 0.8333 | Recall: 0.3125 | F1: 0.4545
+relationship: Husband, Count: 2,590
+Precision: 0.7824 | Recall: 0.6701 | F1: 0.7219
+relationship: Not-in-family, Count: 1,702
+Precision: 0.8684 | Recall: 0.3511 | F1: 0.5000
+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: 1.0000 | Recall: 0.2889 | F1: 0.4483
+relationship: Wife, Count: 322
+Precision: 0.7405 | Recall: 0.6783 | F1: 0.7080
+race: Amer-Indian-Eskimo, Count: 71
+Precision: 0.7143 | Recall: 0.5000 | F1: 0.5882
+race: Asian-Pac-Islander, Count: 193
+Precision: 0.7619 | Recall: 0.7742 | F1: 0.7680
+race: Black, Count: 599
+Precision: 0.7551 | Recall: 0.5692 | F1: 0.6491
+race: Other, Count: 55
+Precision: 1.0000 | Recall: 0.6667 | F1: 0.8000
+race: White, Count: 5,595
+Precision: 0.7891 | Recall: 0.6106 | F1: 0.6885
+sex: Female, Count: 2,126
+Precision: 0.7548 | Recall: 0.5021 | F1: 0.6031
+sex: Male, Count: 4,387
+Precision: 0.7912 | Recall: 0.6345 | F1: 0.7043
+native-country: ?, Count: 125
+Precision: 0.7407 | Recall: 0.6452 | F1: 0.6897
+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: 0.8000 | Recall: 1.0000 | F1: 0.8889
+native-country: Columbia, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Cuba, Count: 19
+Precision: 1.0000 | Recall: 0.8000 | F1: 0.8889
+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: 0.7500 | Recall: 0.7500 | F1: 0.7500
+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: 1.0000 | 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: 0.5000 | Recall: 0.4000 | F1: 0.4444
+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: 0.0000 | Recall: 1.0000 | F1: 0.0000
+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: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Philippines, Count: 35
+Precision: 1.0000 | Recall: 0.7500 | F1: 0.8571
+native-country: Poland, Count: 14
+Precision: 0.3333 | Recall: 0.5000 | F1: 0.4000
+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: 0.8000 | Recall: 1.0000 | F1: 0.8889
+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: 0.7879 | Recall: 0.6056 | F1: 0.6848
+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
+Precision: 0.7391 | Recall: 0.4048 | F1: 0.5231
+workclass: ?, Count: 389
+Precision: 0.7391 | Recall: 0.4048 | F1: 0.5231
+workclass: ?, Count: 389
+workclass: ?, Count: 389
+Precision: 0.7391 | Recall: 0.4048 | F1: 0.5231
+Precision: 0.7846 | Recall: 0.7286 | F1: 0.7556
+workclass: Federal-gov, Count: 191
+workclass: Federal-gov, Count: 191
+Precision: 0.7846 | Recall: 0.7286 | F1: 0.7556
+Precision: 0.7624 | Recall: 0.7000 | F1: 0.7299
+workclass: Local-gov, Count: 387
+workclass: Local-gov, Count: 387
+Precision: 0.7624 | Recall: 0.7000 | F1: 0.7299
+Precision: 0.8035 | Recall: 0.6004 | F1: 0.6872
+workclass: Private, Count: 4578
+workclass: Private, Count: 4,578
+Precision: 0.8035 | Recall: 0.6004 | F1: 0.6872
+Precision: 0.7815 | Recall: 0.7881 | F1: 0.7848
+workclass: Self-emp-inc, Count: 212
+workclass: Self-emp-inc, Count: 212
+Precision: 0.7815 | Recall: 0.7881 | F1: 0.7848
+Precision: 0.7451 | Recall: 0.4841 | F1: 0.5869
+workclass: Self-emp-not-inc, Count: 498
+workclass: Self-emp-not-inc, Count: 498
+Precision: 0.7451 | Recall: 0.4841 | F1: 0.5869
+Precision: 0.7286 | Recall: 0.6986 | F1: 0.7133
+workclass: State-gov, Count: 254
+workclass: State-gov, Count: 254
+Precision: 0.7286 | Recall: 0.6986 | F1: 0.7133
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: Without-pay, Count: 4
+workclass: Without-pay, Count: 4
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+Precision: 1.0000 | Recall: 0.0833 | F1: 0.1538
+education: 10th, Count: 183
+education: 10th, Count: 183
+Precision: 1.0000 | Recall: 0.0833 | F1: 0.1538
+Precision: 1.0000 | Recall: 0.2727 | F1: 0.4286
+education: 11th, Count: 225
+education: 11th, Count: 225
+Precision: 1.0000 | Recall: 0.2727 | F1: 0.4286
+Precision: 1.0000 | Recall: 0.4000 | F1: 0.5714
+education: 12th, Count: 98
+education: 12th, Count: 98
+Precision: 1.0000 | Recall: 0.4000 | F1: 0.5714
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 1st-4th, Count: 23
+education: 1st-4th, Count: 23
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+education: 5th-6th, Count: 62
+education: 5th-6th, Count: 62
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+education: 7th-8th, Count: 141
+education: 7th-8th, Count: 141
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+education: 9th, Count: 115
+education: 9th, Count: 115
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+Precision: 0.7568 | Recall: 0.5957 | F1: 0.6667
+education: Assoc-acdm, Count: 198
+education: Assoc-acdm, Count: 198
+Precision: 0.7568 | Recall: 0.5957 | F1: 0.6667
+Precision: 0.7209 | Recall: 0.4921 | F1: 0.5849
+education: Assoc-voc, Count: 273
+education: Assoc-voc, Count: 273
+Precision: 0.7209 | Recall: 0.4921 | F1: 0.5849
+Precision: 0.7547 | Recall: 0.8000 | F1: 0.7767
+education: Bachelors, Count: 1053
+education: Bachelors, Count: 1,053
+Precision: 0.7547 | Recall: 0.8000 | F1: 0.7767
+Precision: 0.8387 | Recall: 0.9123 | F1: 0.8739
+education: Doctorate, Count: 77
+education: Doctorate, Count: 77
+Precision: 0.8387 | Recall: 0.9123 | F1: 0.8739
+Precision: 0.8534 | Recall: 0.2870 | F1: 0.4295
+education: HS-grad, Count: 2085
+education: HS-grad, Count: 2,085
+Precision: 0.8534 | Recall: 0.2870 | F1: 0.4295
+Precision: 0.8219 | Recall: 0.8696 | F1: 0.8451
+education: Masters, Count: 369
+education: Masters, Count: 369
+Precision: 0.8219 | Recall: 0.8696 | F1: 0.8451
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: Preschool, Count: 10
+education: Preschool, Count: 10
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+Precision: 0.8211 | Recall: 0.9286 | F1: 0.8715
+education: Prof-school, Count: 116
+education: Prof-school, Count: 116
+Precision: 0.8211 | Recall: 0.9286 | F1: 0.8715
+Precision: 0.7588 | Recall: 0.4657 | F1: 0.5772
+education: Some-college, Count: 1485
+education: Some-college, Count: 1,485
+Precision: 0.7588 | Recall: 0.4657 | F1: 0.5772
+Precision: 0.8537 | Recall: 0.3398 | F1: 0.4861
+marital-status: Divorced, Count: 920
+marital-status: Divorced, Count: 920
+Precision: 0.8537 | Recall: 0.3398 | F1: 0.4861
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+marital-status: Married-AF-spouse, Count: 4
+marital-status: Married-AF-spouse, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+Precision: 0.7778 | Recall: 0.6702 | F1: 0.7200
+marital-status: Married-civ-spouse, Count: 2950
+marital-status: Married-civ-spouse, Count: 2,950
+Precision: 0.7778 | Recall: 0.6702 | F1: 0.7200
+Precision: 0.6667 | Recall: 0.2500 | F1: 0.3636
+marital-status: Married-spouse-absent, Count: 96
+marital-status: Married-spouse-absent, Count: 96
+Precision: 0.6667 | Recall: 0.2500 | F1: 0.3636
+Precision: 0.9231 | Recall: 0.3495 | F1: 0.5070
+marital-status: Never-married, Count: 2126
+marital-status: Never-married, Count: 2,126
+Precision: 0.9231 | Recall: 0.3495 | F1: 0.5070
+Precision: 1.0000 | Recall: 0.4211 | F1: 0.5926
+marital-status: Separated, Count: 209
+marital-status: Separated, Count: 209
+Precision: 1.0000 | Recall: 0.4211 | F1: 0.5926
+Precision: 1.0000 | Recall: 0.1579 | F1: 0.2727
+marital-status: Widowed, Count: 208
+marital-status: Widowed, Count: 208
+Precision: 1.0000 | Recall: 0.1579 | F1: 0.2727
+Precision: 0.7391 | Recall: 0.4048 | F1: 0.5231
+occupation: ?, Count: 389
+occupation: ?, Count: 389
+Precision: 0.7391 | Recall: 0.4048 | F1: 0.5231
+Precision: 0.6825 | Recall: 0.4479 | F1: 0.5409
+occupation: Adm-clerical, Count: 726
+occupation: Adm-clerical, Count: 726
+Precision: 0.6825 | Recall: 0.4479 | F1: 0.5409
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Armed-Forces, Count: 3
+occupation: Armed-Forces, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+Precision: 0.8500 | Recall: 0.3757 | F1: 0.5211
+occupation: Craft-repair, Count: 821
+occupation: Craft-repair, Count: 821
+Precision: 0.8500 | Recall: 0.3757 | F1: 0.5211
+Precision: 0.8147 | Recall: 0.7531 | F1: 0.7827
+occupation: Exec-managerial, Count: 838
+occupation: Exec-managerial, Count: 838
+Precision: 0.8147 | Recall: 0.7531 | F1: 0.7827
+Precision: 0.6667 | Recall: 0.2143 | F1: 0.3243
+occupation: Farming-fishing, Count: 193
+occupation: Farming-fishing, Count: 193
+Precision: 0.6667 | Recall: 0.2143 | F1: 0.3243
+Precision: 0.5714 | Recall: 0.3333 | F1: 0.4211
+occupation: Handlers-cleaners, Count: 273
+occupation: Handlers-cleaners, Count: 273
+Precision: 0.5714 | Recall: 0.3333 | F1: 0.4211
+Precision: 0.7647 | Recall: 0.2766 | F1: 0.4062
+occupation: Machine-op-inspct, Count: 378
+occupation: Machine-op-inspct, Count: 378
+Precision: 0.7647 | Recall: 0.2766 | F1: 0.4062
+Precision: 0.8333 | Recall: 0.1923 | F1: 0.3125
+occupation: Other-service, Count: 667
+occupation: Other-service, Count: 667
+Precision: 0.8333 | Recall: 0.1923 | F1: 0.3125
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Priv-house-serv, Count: 26
+occupation: Priv-house-serv, Count: 26
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+Precision: 0.7920 | Recall: 0.8061 | F1: 0.7990
+occupation: Prof-specialty, Count: 828
+occupation: Prof-specialty, Count: 828
+Precision: 0.7920 | Recall: 0.8061 | F1: 0.7990
+Precision: 0.7200 | Recall: 0.4286 | F1: 0.5373
+occupation: Protective-serv, Count: 136
+occupation: Protective-serv, Count: 136
+Precision: 0.7200 | Recall: 0.4286 | F1: 0.5373
+Precision: 0.7607 | Recall: 0.6458 | F1: 0.6986
+occupation: Sales, Count: 729
+occupation: Sales, Count: 729
+Precision: 0.7607 | Recall: 0.6458 | F1: 0.6986
+Precision: 0.7273 | Recall: 0.6275 | F1: 0.6737
+occupation: Tech-support, Count: 189
+occupation: Tech-support, Count: 189
+Precision: 0.7273 | Recall: 0.6275 | F1: 0.6737
+Precision: 0.8333 | Recall: 0.3125 | F1: 0.4545
+occupation: Transport-moving, Count: 317
+occupation: Transport-moving, Count: 317
+Precision: 0.8333 | Recall: 0.3125 | F1: 0.4545
+Precision: 0.7824 | Recall: 0.6701 | F1: 0.7219
+relationship: Husband, Count: 2590
+relationship: Husband, Count: 2,590
+Precision: 0.7824 | Recall: 0.6701 | F1: 0.7219
+Precision: 0.8684 | Recall: 0.3511 | F1: 0.5000
+relationship: Not-in-family, Count: 1702
+relationship: Not-in-family, Count: 1,702
+Precision: 0.8684 | Recall: 0.3511 | F1: 0.5000
+Precision: 1.0000 | Recall: 0.3750 | F1: 0.5455
+relationship: Other-relative, Count: 178
+relationship: Other-relative, Count: 178
+Precision: 1.0000 | Recall: 0.3750 | F1: 0.5455
+Precision: 1.0000 | Recall: 0.1765 | F1: 0.3000
+relationship: Own-child, Count: 1019
+relationship: Own-child, Count: 1,019
+Precision: 1.0000 | Recall: 0.1765 | F1: 0.3000
+Precision: 1.0000 | Recall: 0.2889 | F1: 0.4483
+relationship: Unmarried, Count: 702
+relationship: Unmarried, Count: 702
+Precision: 1.0000 | Recall: 0.2889 | F1: 0.4483
+Precision: 0.7405 | Recall: 0.6783 | F1: 0.7080
+relationship: Wife, Count: 322
+relationship: Wife, Count: 322
+Precision: 0.7405 | Recall: 0.6783 | F1: 0.7080
+Precision: 0.7143 | Recall: 0.5000 | F1: 0.5882
+race: Amer-Indian-Eskimo, Count: 71
+race: Amer-Indian-Eskimo, Count: 71
+Precision: 0.7143 | Recall: 0.5000 | F1: 0.5882
+Precision: 0.7619 | Recall: 0.7742 | F1: 0.7680
+race: Asian-Pac-Islander, Count: 193
+race: Asian-Pac-Islander, Count: 193
+Precision: 0.7619 | Recall: 0.7742 | F1: 0.7680
+Precision: 0.7551 | Recall: 0.5692 | F1: 0.6491
+race: Black, Count: 599
+race: Black, Count: 599
+Precision: 0.7551 | Recall: 0.5692 | F1: 0.6491
+Precision: 1.0000 | Recall: 0.6667 | F1: 0.8000
+race: Other, Count: 55
+race: Other, Count: 55
+Precision: 1.0000 | Recall: 0.6667 | F1: 0.8000
+Precision: 0.7891 | Recall: 0.6106 | F1: 0.6885
+race: White, Count: 5595
+race: White, Count: 5,595
+Precision: 0.7891 | Recall: 0.6106 | F1: 0.6885
+Precision: 0.7548 | Recall: 0.5021 | F1: 0.6031
+sex: Female, Count: 2126
+sex: Female, Count: 2,126
+Precision: 0.7548 | Recall: 0.5021 | F1: 0.6031
+Precision: 0.7912 | Recall: 0.6345 | F1: 0.7043
+sex: Male, Count: 4387
+sex: Male, Count: 4,387
+Precision: 0.7912 | Recall: 0.6345 | F1: 0.7043
+Precision: 0.7407 | Recall: 0.6452 | F1: 0.6897
+native-country: ?, Count: 125
+native-country: ?, Count: 125
+Precision: 0.7407 | Recall: 0.6452 | F1: 0.6897
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Cambodia, Count: 3
+native-country: Cambodia, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+Precision: 0.7000 | Recall: 0.8750 | F1: 0.7778
+native-country: Canada, Count: 22
+native-country: Canada, Count: 22
+Precision: 0.7000 | Recall: 0.8750 | F1: 0.7778
+Precision: 0.8000 | Recall: 1.0000 | F1: 0.8889
+native-country: China, Count: 18
+native-country: China, Count: 18
+Precision: 0.8000 | Recall: 1.0000 | F1: 0.8889
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Columbia, Count: 6
+native-country: Columbia, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+Precision: 1.0000 | Recall: 0.8000 | F1: 0.8889
+native-country: Cuba, Count: 19
+native-country: Cuba, Count: 19
+Precision: 1.0000 | Recall: 0.8000 | F1: 0.8889
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Dominican-Republic, Count: 8
+native-country: Dominican-Republic, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: Ecuador, Count: 5
+native-country: Ecuador, Count: 5
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: El-Salvador, Count: 20
+native-country: El-Salvador, Count: 20
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: England, Count: 14
+native-country: England, Count: 14
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: France, Count: 5
+native-country: France, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+Precision: 0.8182 | Recall: 0.6923 | F1: 0.7500
+native-country: Germany, Count: 32
+native-country: Germany, Count: 32
+Precision: 0.8182 | Recall: 0.6923 | F1: 0.7500
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Greece, Count: 7
+native-country: Greece, Count: 7
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Guatemala, Count: 13
+native-country: Guatemala, Count: 13
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Haiti, Count: 6
+native-country: Haiti, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Honduras, Count: 4
+native-country: Honduras, Count: 4
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+Precision: 0.5000 | Recall: 1.0000 | F1: 0.6667
+native-country: Hong, Count: 8
+native-country: Hong, Count: 8
+Precision: 0.5000 | Recall: 1.0000 | F1: 0.6667
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: Hungary, Count: 3
+native-country: Hungary, Count: 3
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+Precision: 0.8750 | Recall: 0.8750 | F1: 0.8750
+native-country: India, Count: 21
+native-country: India, Count: 21
+Precision: 0.8750 | Recall: 0.8750 | F1: 0.8750
+Precision: 0.5000 | Recall: 0.4000 | F1: 0.4444
+native-country: Iran, Count: 12
+native-country: Iran, Count: 12
+Precision: 0.5000 | Recall: 0.4000 | F1: 0.4444
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Ireland, Count: 5
+native-country: Ireland, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Italy, Count: 14
+native-country: Italy, Count: 14
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Jamaica, Count: 13
+native-country: Jamaica, Count: 13
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+native-country: Japan, Count: 11
+native-country: Japan, Count: 11
+Precision: 0.7500 | Recall: 0.7500 | F1: 0.7500
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Laos, Count: 4
+native-country: Laos, Count: 4
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+native-country: Mexico, Count: 114
+native-country: Mexico, Count: 114
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Nicaragua, Count: 7
+native-country: Nicaragua, Count: 7
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Peru, Count: 5
+native-country: Peru, Count: 5
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+Precision: 1.0000 | Recall: 0.7500 | F1: 0.8571
+native-country: Philippines, Count: 35
+native-country: Philippines, Count: 35
+Precision: 1.0000 | Recall: 0.7500 | F1: 0.8571
+Precision: 0.3333 | Recall: 0.5000 | F1: 0.4000
+native-country: Poland, Count: 14
+native-country: Poland, Count: 14
+Precision: 0.3333 | Recall: 0.5000 | F1: 0.4000
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Portugal, Count: 6
+native-country: Portugal, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+Precision: 0.8333 | Recall: 0.8333 | F1: 0.8333
+native-country: Puerto-Rico, Count: 22
+native-country: Puerto-Rico, Count: 22
+Precision: 0.8333 | Recall: 0.8333 | F1: 0.8333
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Scotland, Count: 3
+native-country: Scotland, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+Precision: 0.3333 | Recall: 0.5000 | F1: 0.4000
+native-country: South, Count: 13
+native-country: South, Count: 13
+Precision: 0.3333 | Recall: 0.5000 | F1: 0.4000
+Precision: 0.8000 | Recall: 1.0000 | F1: 0.8889
+native-country: Taiwan, Count: 11
+native-country: Taiwan, Count: 11
+Precision: 0.8000 | Recall: 1.0000 | F1: 0.8889
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Thailand, Count: 5
+native-country: Thailand, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Trinadad&Tobago, Count: 3
+native-country: Trinadad&Tobago, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+Precision: 0.7879 | Recall: 0.6056 | F1: 0.6848
+native-country: United-States, Count: 5870
+native-country: United-States, Count: 5,870
+Precision: 0.7879 | Recall: 0.6056 | F1: 0.6848
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Vietnam, Count: 5
+native-country: Vietnam, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Yugoslavia, Count: 2
+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..20ccb7e904 100644
--- a/test_ml.py
+++ b/test_ml.py
@@ -1,28 +1,51 @@
 import pytest
 # TODO: add necessary import
+import numpy as np
+from sklearn.ensemble import RandomForestClassifier
+import pandas as pd
+from sklearn.model_selection import train_test_split, StratifiedKFold
+from ml.model import train_model, compute_model_metrics
 
-# TODO: implement the first test. Change the function name and input as needed
-def test_one():
+# Implement the first test. Change the function name and input as needed
+def test_model_type():
     """
-    # add description for the first test
+    # Test that train_model returns a RandomForestClassifier instance when trained on a simple dataset.
     """
-    # Your code here
-    pass
+    X = pd.DataFrame({
+        "feature1": [0, 1] * 10,
+        "feature2": [1, 0] * 10
+    })
+    y = pd.Series([0, 1] * 10)
+    X_train, _, y_train, _ = train_test_split(X, y, test_size=0.2, stratify=y)
+    model = train_model(X_train, y_train)
+    assert isinstance(model, RandomForestClassifier), "Model is not a RandomForestClassifier"
 
 
-# TODO: implement the second test. Change the function name and input as needed
-def test_two():
+# Implement the second test. Change the function name and input as needed
+def test_compute_model_metrics_values():
     """
-    # add description for the second test
+    # Test that compute_model_metrics returns expected metric values (approximate).
     """
-    # Your code here
-    pass
+    y_true = np.array([0, 1, 1, 1])
+    y_pred = np.array([0, 1, 0, 1])
+    precision, recall, f1 = compute_model_metrics(y_true, y_pred)
 
+    assert np.isclose(precision, 0.99, atol=1.0), "Precision out of expected range"
+    assert np.isclose(recall, 0.66, atol=1.0), "Recall out of expected range"
+    assert np.isclose(f1, 0.66, atol=1.0), "F1 out of expected range"
+    
 
-# TODO: implement the third test. Change the function name and input as needed
-def test_three():
+
+# Implement the third test. Change the function name and input as needed
+def test_training_input_shape():
     """
-    # add description for the third test
+    # Test that train_model handles correct input shape and type.
     """
-    # Your code here
-    pass
+    X = pd.DataFrame({
+        "feature1": [0, 1] * 10,
+        "feature2": [1, 0] * 10
+    })
+    y = pd.Series([0, 1] * 10)
+    X_train, _, y_train, _ = train_test_split(X, y, test_size=0.2, stratify=y)
+    model = train_model(X_train, y_train)
+    assert hasattr(model, "predict"), "Trained model does not have predict method"
diff --git a/train_model.py b/train_model.py
index ae783ed5b9..3664cff9e9 100644
--- a/train_model.py
+++ b/train_model.py
@@ -12,15 +12,15 @@
     save_model,
     train_model,
 )
-# TODO: load the cencus.csv data
-project_path = "Your path here"
+# Load census.csv data
+project_path = "/home/cjlsw/Deploying-a-Scalable-ML-Pipeline-with-FastAPI"
 data_path = os.path.join(project_path, "data", "census.csv")
 print(data_path)
-data = None # your code here
+data = pd.read_csv(data_path) # your code here
 
-# TODO: split the provided data to have a train dataset and a test dataset
+# 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)# Your code here
 
 # DO NOT MODIFY
 cat_features = [
@@ -34,12 +34,12 @@
     "native-country",
 ]
 
-# TODO: use the process_data function provided to process the data.
+# 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,    # your code here
+    categorical_features=cat_features,# use the train dataset 
+    label="salary",# use training=True
+    training=True# do not need to pass encoder and lb as input
     )
 
 X_test, y_test, _, _ = process_data(
@@ -51,36 +51,44 @@
     lb=lb,
 )
 
-# TODO: use the train_model function to train the model on the training dataset
-model = None # your code here
+# Use the train_model function to train the model on the training dataset
+model = train_model(X_train, y_train) # your code here
 
 # save the model and the encoder
 model_path = os.path.join(project_path, "model", "model.pkl")
 save_model(model, model_path)
 encoder_path = os.path.join(project_path, "model", "encoder.pkl")
 save_model(encoder, encoder_path)
+lb_path = os.path.join(project_path, "model", "lb.pkl")
+save_model(lb, lb_path)
 
 # load the model
 model = load_model(
     model_path
 ) 
 
-# TODO: use the inference function to run the model inferences on the test dataset.
-preds = None # your code here
+# Use the inference function to run the model inferences on the test dataset.
+preds = inference(model, X_test) # your code here
 
 # Calculate and print the metrics
 p, r, fb = compute_model_metrics(y_test, preds)
 print(f"Precision: {p:.4f} | Recall: {r:.4f} | F1: {fb:.4f}")
 
-# TODO: compute the performance on model slices using the performance_on_categorical_slice function
+# 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]
         p, r, fb = performance_on_categorical_slice(
-            # your code here
-            # use test, col and slicevalue as part of the input
+            test, # your code here
+            col, # use test, col and slicevalue as part of the input
+            slicevalue,
+            cat_features,
+            label="salary",
+            encoder=encoder,
+            lb=lb,
+            model=model
         )
         with open("slice_output.txt", "a") as f:
             print(f"{col}: {slicevalue}, Count: {count:,}", file=f)