diff --git a/local_api.py b/local_api.py
index a3bff2f988..5ad4eb0a7b 100644
--- a/local_api.py
+++ b/local_api.py
@@ -1,17 +1,17 @@
 import json
-
 import requests
 
-# TODO: send a GET using the URL http://127.0.0.1:8000
-r = None # Your code here
+BASE_URL = "http://127.0.0.1:8000"
 
-# TODO: print the status code
-# print()
-# TODO: print the welcome message
-# print()
+# ---- GET request ----------------------------------------------------------
+r = requests.get(f"{BASE_URL}/")
 
+# print status code and welcome message
+print("GET / status:", r.status_code)
+print("GET / response:", r.json())
 
 
+# ---- POST request ---------------------------------------------------------
 data = {
     "age": 37,
     "workclass": "Private",
@@ -29,10 +29,7 @@
     "native-country": "United-States",
 }
 
-# TODO: send a POST using the data above
-r = None # Your code here
+r = requests.post(f"{BASE_URL}/predict/", json=data)
 
-# TODO: print the status code
-# print()
-# TODO: print the result
-# print()
+print("POST /predict/ status:", r.status_code)
+print("POST /predict/ response:", r.json())
diff --git a/main.py b/main.py
index 638e2414de..53a058816c 100644
--- a/main.py
+++ b/main.py
@@ -7,6 +7,7 @@
 from ml.data import apply_label, process_data
 from ml.model import inference, load_model
 
+
 # DO NOT MODIFY
 class Data(BaseModel):
     age: int = Field(..., example=37)
@@ -26,25 +27,32 @@ 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)
 
-path = None # TODO: enter the path for the saved model 
-model = load_model(path)
+# ---- load saved artifacts -------------------------------------------------
+project_path = "."
+model_dir = os.path.join(project_path, "model")
+
+encoder_path = os.path.join(model_dir, "encoder.pkl")
+lb_path = os.path.join(model_dir, "lb.pkl")
+model_path = os.path.join(model_dir, "model.pkl")
+
+encoder = load_model(encoder_path)
+lb = load_model(lb_path)
+model = load_model(model_path)
+
+# ---- create FastAPI app ---------------------------------------------------
+app = FastAPI(title="Income Classification API")
 
-# TODO: create a RESTful API using FastAPI
-app = None # your code here
 
-# TODO: create a GET on the root giving a welcome message
+# ---- root endpoint --------------------------------------------------------
 @app.get("/")
 async def get_root():
-    """ Say hello!"""
-    # your code here
-    pass
+    """Simple welcome endpoint."""
+    return {"message": "Hello from the income classification API!"}
 
 
-# TODO: create a POST on a different path that does model inference
-@app.post("/data/")
+# ---- prediction endpoint --------------------------------------------------
+@app.post("/predict/")
 async def post_inference(data: Data):
     # DO NOT MODIFY: turn the Pydantic model into a dict.
     data_dict = data.dict()
@@ -64,11 +72,21 @@ async def post_inference(data: Data):
         "sex",
         "native-country",
     ]
+
+    # process data for inference
     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,
+        label=None,
+        training=False,
+        encoder=encoder,
+        lb=lb,
     )
-    _inference = None # your code here to predict the result using data_processed
-    return {"result": apply_label(_inference)}
+
+    # make prediction
+    preds = inference(model, data_processed)
+    _inference = preds[0]
+
+    # convert 0/1 to <=50K />50K label
+    # NOTE: apply_label expects an indexable input, so wrap in a list
+    return {"result": apply_label([_inference])}
diff --git a/ml/model.py b/ml/model.py
index f361110f18..d676fa4929 100644
--- a/ml/model.py
+++ b/ml/model.py
@@ -1,9 +1,12 @@
 import pickle
+import numpy as np
+
+from sklearn.ensemble import RandomForestClassifier
 from sklearn.metrics import fbeta_score, precision_score, recall_score
+
 from ml.data import process_data
-# TODO: add necessary import
 
-# Optional: implement hyperparameter tuning.
+
 def train_model(X_train, y_train):
     """
     Trains a machine learning model and returns it.
@@ -19,26 +22,22 @@ def train_model(X_train, y_train):
     model
         Trained machine learning model.
     """
-    # TODO: implement the function
-    pass
+    model = RandomForestClassifier(
+        n_estimators=100,
+        random_state=42,
+        n_jobs=-1,
+    )
+    model.fit(X_train, y_train)
+    return model
 
 
 def compute_model_metrics(y, preds):
     """
     Validates the trained machine learning model using precision, recall, and F1.
-
-    Inputs
-    ------
-    y : np.array
-        Known labels, binarized.
-    preds : np.array
-        Predicted labels, binarized.
-    Returns
-    -------
-    precision : float
-    recall : float
-    fbeta : float
     """
+    y = np.array(y).reshape(-1)
+    preds = np.array(preds).reshape(-1)
+
     fbeta = fbeta_score(y, preds, beta=1, zero_division=1)
     precision = precision_score(y, preds, zero_division=1)
     recall = recall_score(y, preds, zero_division=1)
@@ -46,83 +45,51 @@ def compute_model_metrics(y, preds):
 
 
 def inference(model, X):
-    """ Run model inferences and return the predictions.
-
-    Inputs
-    ------
-    model : ???
-        Trained machine learning model.
-    X : np.array
-        Data used for prediction.
-    Returns
-    -------
-    preds : np.array
-        Predictions from the model.
     """
-    # TODO: implement the function
-    pass
+    Run model inferences and return the predictions.
+    """
+    preds = model.predict(X)
+    return np.array(preds).reshape(-1)
 
-def save_model(model, path):
-    """ Serializes model to a file.
 
-    Inputs
-    ------
-    model
-        Trained machine learning model or OneHotEncoder.
-    path : str
-        Path to save pickle file.
+def save_model(model, path):
+    """
+    Serializes model or encoder or label binarizer to a file.
     """
-    # TODO: implement the function
-    pass
+    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:
+        obj = pickle.load(f)
+    return obj
 
 
 def performance_on_categorical_slice(
     data, column_name, slice_value, categorical_features, label, encoder, lb, model
 ):
-    """ Computes the model metrics on a slice of the data specified by a column name and
-
-    Processes the data using one hot encoding for the categorical features and a
-    label binarizer for the labels. This can be used in either training or
-    inference/validation.
+    """
+    Computes the model metrics on a slice of the data specified by a column name
+    and slice value.
+    """
+    slice_data = data[data[column_name] == slice_value]
 
-    Inputs
-    ------
-    data : pd.DataFrame
-        Dataframe containing the features and label. Columns in `categorical_features`
-    column_name : str
-        Column containing the sliced feature.
-    slice_value : str, int, float
-        Value of the slice feature.
-    categorical_features: list
-        List containing the names of the categorical features (default=[])
-    label : str
-        Name of the label column in `X`. If None, then an empty array will be returned
-        for y (default=None)
-    encoder : sklearn.preprocessing._encoders.OneHotEncoder
-        Trained sklearn OneHotEncoder, only used if training=False.
-    lb : sklearn.preprocessing._label.LabelBinarizer
-        Trained sklearn LabelBinarizer, only used if training=False.
-    model : ???
-        Model used for the task.
+    if slice_data.empty:
+        return 0.0, 0.0, 0.0
 
-    Returns
-    -------
-    precision : float
-    recall : float
-    fbeta : float
-
-    """
-    # 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
+        slice_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..ba9c2135af
Binary files /dev/null and b/model/encoder.pkl differ
diff --git a/model/lb.pkl b/model/lb.pkl
new file mode 100644
index 0000000000..510550bf3e
Binary files /dev/null and b/model/lb.pkl differ
diff --git a/model/model.pkl b/model/model.pkl
new file mode 100644
index 0000000000..10d3212a5c
Binary files /dev/null and b/model/model.pkl differ
diff --git a/model_card_template.md b/model_card_template.md
index 0392f3b9eb..d42c143a15 100644
--- a/model_card_template.md
+++ b/model_card_template.md
@@ -2,17 +2,86 @@
 
 For additional information see the Model Card paper: https://arxiv.org/pdf/1810.03993.pdf
 
+---
+
 ## Model Details
 
+This project uses a Random Forest classifier implemented with scikit learn.  
+The purpose of the model is to predict whether an individual earns more than fifty thousand dollars per year.  
+The model was trained locally using Python 3.10 as part of the Udacity Machine Learning DevOps project.  
+
+The model is saved as `model/model.pkl` and the preprocessing objects (encoder and label binarizer) are stored in the same folder.
+
+---
+
 ## Intended Use
 
+This model is intended purely for educational and demonstration purposes.  
+It shows how to build an end to end ML pipeline including data processing, model training, automated testing, performance evaluation, CI, and API deployment with FastAPI.
+
+It is **not** intended for real world decision making in hiring, lending, housing, insurance, or other areas that affect people’s lives.
+
+---
+
 ## Training Data
 
+The model was trained on the provided **Census Income dataset**, which contains demographic and employment related features.  
+Features include workclass, education, marital status, occupation, race, sex, and native country.
+
+Eighty percent of the dataset is used for training.  
+Categorical features are one hot encoded using the provided `process_data` function.
+
+---
+
 ## Evaluation Data
 
+Twenty percent of the dataset is held out as a test set.  
+The same preprocessing pipeline (encoder and label binarizer) is applied to the test data.
+
+Evaluation includes both overall performance on the test set and performance on slices of data across individual categories (workclass, sex, race, etc.).  
+Slice performance is saved to `slice_output.txt`.
+
+---
+
 ## Metrics
-_Please include the metrics used and your model's performance on those metrics._
+
+Overall performance on the held out test set:
+
+- **Precision:** 0.7419  
+- **Recall:** 0.6384  
+- **F1 Score:** 0.6863  
+
+Slice performance varies across groups. Examples include:
+
+- **Sex = Male:** Higher recall and F1 compared to female  
+- **Workclass = Private:** Moderate precision and recall  
+- **Education levels:** Significant variation depending on category  
+
+These results indicate that model performance is not uniform across demographic groups.
+
+---
 
 ## Ethical Considerations
 
+The Census dataset contains real world demographic information and may reflect historical social biases.  
+A model trained on such data can inherit or amplify these biases.  
+Because of this, the model should **not** be used for any real decision making that can affect individuals.
+
+Potential risks include:
+
+- Unequal performance for different demographic groups  
+- Unintended discrimination if used in hiring or income related screening  
+- Misinterpretation of model predictions due to limited context  
+
+Care must be taken to avoid misuse.
+
+---
+
 ## Caveats and Recommendations
+
+- The model is not tuned or optimized; default Random Forest parameters are used.  
+- Data is outdated and does not reflect current populations.  
+- Model should be used only for learning purposes.  
+- If extended further, a fairness analysis, hyperparameter tuning, and updated datasets are recommended.  
+
+The model should always be accompanied by transparency about its limitations and intended educational use.
diff --git a/screenshots/continuous_integration.png b/screenshots/continuous_integration.png
new file mode 100644
index 0000000000..b92bd71819
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..05362e059c
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..de81ea496e
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..d52839dd53
--- /dev/null
+++ b/slice_output.txt
@@ -0,0 +1,198 @@
+workclass: ?, Count: 389
+Precision: 0.6538 | Recall: 0.4048 | F1: 0.5000
+workclass: Federal-gov, Count: 191
+Precision: 0.7971 | Recall: 0.7857 | F1: 0.7914
+workclass: Local-gov, Count: 387
+Precision: 0.7576 | Recall: 0.6818 | F1: 0.7177
+workclass: Private, Count: 4,578
+Precision: 0.7376 | Recall: 0.6404 | F1: 0.6856
+workclass: Self-emp-inc, Count: 212
+Precision: 0.7807 | Recall: 0.7542 | F1: 0.7672
+workclass: Self-emp-not-inc, Count: 498
+Precision: 0.7064 | Recall: 0.4904 | F1: 0.5789
+workclass: State-gov, Count: 254
+Precision: 0.7424 | Recall: 0.6712 | F1: 0.7050
+workclass: Without-pay, Count: 4
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 10th, Count: 183
+Precision: 0.4000 | Recall: 0.1667 | F1: 0.2353
+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: 0.0000 | 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: 0.7000 | Recall: 0.5957 | F1: 0.6437
+education: Assoc-voc, Count: 273
+Precision: 0.6471 | Recall: 0.5238 | F1: 0.5789
+education: Bachelors, Count: 1,053
+Precision: 0.7523 | Recall: 0.7289 | F1: 0.7404
+education: Doctorate, Count: 77
+Precision: 0.8644 | Recall: 0.8947 | F1: 0.8793
+education: HS-grad, Count: 2,085
+Precision: 0.6594 | Recall: 0.4377 | F1: 0.5261
+education: Masters, Count: 369
+Precision: 0.8271 | Recall: 0.8551 | F1: 0.8409
+education: Preschool, Count: 10
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: Prof-school, Count: 116
+Precision: 0.8182 | Recall: 0.9643 | F1: 0.8852
+education: Some-college, Count: 1,485
+Precision: 0.6857 | Recall: 0.5199 | F1: 0.5914
+marital-status: Divorced, Count: 920
+Precision: 0.7600 | Recall: 0.3689 | F1: 0.4967
+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: 0.7346 | Recall: 0.6900 | F1: 0.7116
+marital-status: Married-spouse-absent, Count: 96
+Precision: 1.0000 | Recall: 0.2500 | F1: 0.4000
+marital-status: Never-married, Count: 2,126
+Precision: 0.8302 | Recall: 0.4272 | F1: 0.5641
+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: 0.6538 | Recall: 0.4048 | F1: 0.5000
+occupation: Adm-clerical, Count: 726
+Precision: 0.6338 | Recall: 0.4688 | F1: 0.5389
+occupation: Armed-Forces, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 821
+Precision: 0.6567 | Recall: 0.4862 | F1: 0.5587
+occupation: Exec-managerial, Count: 838
+Precision: 0.7952 | Recall: 0.7531 | F1: 0.7736
+occupation: Farming-fishing, Count: 193
+Precision: 0.5455 | Recall: 0.2143 | F1: 0.3077
+occupation: Handlers-cleaners, Count: 273
+Precision: 0.5714 | Recall: 0.3333 | F1: 0.4211
+occupation: Machine-op-inspct, Count: 378
+Precision: 0.5938 | Recall: 0.4043 | F1: 0.4810
+occupation: Other-service, Count: 667
+Precision: 1.0000 | Recall: 0.1923 | F1: 0.3226
+occupation: Priv-house-serv, Count: 26
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 828
+Precision: 0.7880 | Recall: 0.7679 | F1: 0.7778
+occupation: Protective-serv, Count: 136
+Precision: 0.7353 | Recall: 0.5952 | F1: 0.6579
+occupation: Sales, Count: 729
+Precision: 0.7273 | Recall: 0.6667 | F1: 0.6957
+occupation: Tech-support, Count: 189
+Precision: 0.7143 | Recall: 0.6863 | F1: 0.7000
+occupation: Transport-moving, Count: 317
+Precision: 0.6250 | Recall: 0.4688 | F1: 0.5357
+relationship: Husband, Count: 2,590
+Precision: 0.7370 | Recall: 0.6923 | F1: 0.7140
+relationship: Not-in-family, Count: 1,702
+Precision: 0.7959 | Recall: 0.4149 | F1: 0.5455
+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: 0.9231 | Recall: 0.2667 | F1: 0.4138
+relationship: Wife, Count: 322
+Precision: 0.7132 | Recall: 0.6783 | F1: 0.6953
+race: Amer-Indian-Eskimo, Count: 71
+Precision: 0.6250 | Recall: 0.5000 | F1: 0.5556
+race: Asian-Pac-Islander, Count: 193
+Precision: 0.7857 | Recall: 0.7097 | F1: 0.7458
+race: Black, Count: 599
+Precision: 0.7273 | Recall: 0.6154 | F1: 0.6667
+race: Other, Count: 55
+Precision: 1.0000 | Recall: 0.6667 | F1: 0.8000
+race: White, Count: 5,595
+Precision: 0.7404 | Recall: 0.6373 | F1: 0.6850
+sex: Female, Count: 2,126
+Precision: 0.7229 | Recall: 0.5150 | F1: 0.6015
+sex: Male, Count: 4,387
+Precision: 0.7445 | Recall: 0.6599 | F1: 0.6997
+native-country: ?, Count: 125
+Precision: 0.7500 | Recall: 0.6774 | F1: 0.7119
+native-country: Cambodia, Count: 3
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Canada, Count: 22
+Precision: 0.6667 | Recall: 0.7500 | F1: 0.7059
+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: 0.8462 | Recall: 0.8462 | F1: 0.8462
+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: 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: 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: 0.7392 | Recall: 0.6321 | F1: 0.6814
+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..2174148dbd 100644
--- a/test_ml.py
+++ b/test_ml.py
@@ -1,28 +1,101 @@
 import pytest
-# TODO: add necessary import
+import numpy as np
+from sklearn.ensemble import RandomForestClassifier
 
-# TODO: implement the first test. Change the function name and input as needed
-def test_one():
+from ml.data import process_data
+from ml.model import (
+    train_model,
+    inference,
+    compute_model_metrics
+)
+
+
+# ---------------------------------------------------------
+# Test 1: process_data returns correct structure
+# ---------------------------------------------------------
+def test_process_data_shapes():
     """
-    # add description for the first test
+    Ensures that process_data correctly encodes categorical features
+    and splits labels. Checks correct output lengths and array types.
     """
-    # Your code here
-    pass
+    sample = {
+        "age": [25, 40, 60],
+        "workclass": ["Private", "Self-emp", "Private"],
+        "salary": ["<=50K", ">50K", "<=50K"]
+    }
+
+    import pandas as pd
+    df = pd.DataFrame(sample)
+
+    cat_features = ["workclass"]
 
+    X, y, encoder, lb = process_data(
+        df,
+        categorical_features=cat_features,
+        label="salary",
+        training=True
+    )
 
-# TODO: implement the second test. Change the function name and input as needed
-def test_two():
+    # X must be 2D
+    assert len(X.shape) == 2
+    # y must be 1D
+    assert len(y.shape) == 1
+    # lengths must match
+    assert X.shape[0] == y.shape[0]
+    # encoder should not be None
+    assert encoder is not None
+    assert lb is not None
+
+
+# ---------------------------------------------------------
+# Test 2: Model trains and inference returns correct shape
+# ---------------------------------------------------------
+def test_model_training_and_inference():
     """
-    # add description for the second test
+    Ensures that train_model returns a sklearn model and inference()
+    produces predictions of correct shape.
     """
-    # Your code here
-    pass
+    sample = {
+        "age": [22, 45, 37, 52],
+        "workclass": ["Private", "Self-emp", "Private", "State-gov"],
+        "salary": ["<=50K", ">50K", "<=50K", ">50K"]
+    }
+
+    import pandas as pd
+    df = pd.DataFrame(sample)
+
+    cat_features = ["workclass"]
 
+    X, y, encoder, lb = process_data(
+        df,
+        categorical_features=cat_features,
+        label="salary",
+        training=True
+    )
 
-# TODO: implement the third test. Change the function name and input as needed
-def test_three():
+    model = train_model(X, y)
+
+    assert isinstance(model, RandomForestClassifier)
+
+    preds = inference(model, X)
+
+    assert len(preds) == len(y)
+    assert preds.shape == y.shape
+
+
+# ---------------------------------------------------------
+# Test 3: compute_model_metrics outputs valid values
+# ---------------------------------------------------------
+def test_compute_model_metrics_values():
     """
-    # add description for the third test
+    Ensures compute_model_metrics returns precision, recall, and fbeta 
+    scores that fall between 0 and 1.
     """
-    # Your code here
-    pass
+    y_true = np.array([0, 1, 1, 0])
+    y_pred = np.array([0, 1, 0, 0])
+
+    precision, recall, fbeta = compute_model_metrics(y_true, y_pred)
+
+    assert 0 <= precision <= 1
+    assert 0 <= recall <= 1
+    assert 0 <= fbeta <= 1
diff --git a/train_model.py b/train_model.py
index ae783ed5b9..bfe6dba9d3 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 the census.csv data
+project_path = "."  # working directory is the project root
 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
+# Split the data into train and test
+train, test = train_test_split(data, test_size=0.20, random_state=42)
 
 # DO NOT MODIFY
 cat_features = [
@@ -34,13 +34,13 @@
     "native-country",
 ]
 
-# TODO: use the process_data function provided to process the data.
+# Process training and test 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(
     test,
@@ -51,37 +51,50 @@
     lb=lb,
 )
 
-# TODO: use the train_model function to train the model on the training dataset
-model = None # your code here
+# Train the model
+model = train_model(X_train, y_train)
 
-# save the model and the encoder
+# Save the model and the encoder and label binarizer
 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)
 
-# load the model
-model = load_model(
-    model_path
-) 
+lb_path = os.path.join(project_path, "model", "lb.pkl")
+save_model(lb, lb_path)
+
+# Load the model back
+model = load_model(model_path)
 
-# TODO: use the inference function to run the model inferences on the test dataset.
-preds = None # your code here
+# Run inference on the test data
+preds = inference(model, X_test)
 
-# Calculate and print the metrics
+# Calculate and print the overall 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
-# iterate through the categorical features
+# Remove old slice file if it exists so we start fresh
+if os.path.exists("slice_output.txt"):
+    os.remove("slice_output.txt")
+
+# Compute performance on model slices and save to slice_output.txt
 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
+            data=test,
+            column_name=col,
+            slice_value=slicevalue,
+            categorical_features=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)
-            print(f"Precision: {p:.4f} | Recall: {r:.4f} | F1: {fb:.4f}", file=f)
+            print(
+                f"Precision: {p:.4f} | Recall: {r:.4f} | F1: {fb:.4f}",
+                file=f,
+            )