diff --git a/.github/workflows/manual.yml b/.github/workflows/manual.yml
index f43ffbd885..ea5adcf45f 100644
--- a/.github/workflows/manual.yml
+++ b/.github/workflows/manual.yml
@@ -1,31 +1,42 @@
-name: Python CI
+import pickle
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import fbeta_score, precision_score, recall_score
+from ml.data import process_data
 
-on: [push]
+def train_model(X_train, y_train):
+    model = RandomForestClassifier(random_state=42)
+    model.fit(X_train, y_train)
+    return model
 
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version: ["3.10"] 
+def compute_model_metrics(y, preds):
+    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)
+    return precision, recall, fbeta
 
-    steps:
-    - uses: actions/checkout@v2
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v2
-      with:
-        python-version: ${{ matrix.python-version }}
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install flake8 pytest
-        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
-    - name: Lint with flake8
-      run: |
-        # stop the build if there are Python syntax errors or undefined names
-        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
-    - name: Test with pytest
-      run: |
-        pytest test_ml.py -v
+def inference(model, X):
+    return model.predict(X)
+
+def save_model(model, path):
+    with open(path, 'wb') as f:
+        pickle.dump(model, f)
+
+def load_model(path):
+    with open(path, 'rb') as f:
+        return pickle.load(f)
+
+def performance_on_categorical_slice(
+    data, column_name, slice_value, categorical_features, label, encoder, lb, model
+):
+    slice_data = data[data[column_name] == slice_value]
+    X_slice, y_slice, _, _ = process_data(
+        slice_data,
+        categorical_features=categorical_features,
+        label=label,
+        training=False,
+        encoder=encoder,
+        lb=lb
+    )
+    preds = inference(model, X_slice)
+    precision, recall, fbeta = compute_model_metrics(y_slice, preds)
+    return precision, recall, fbeta
diff --git a/.github/workflows/python-app.yml b/.github/workflows/python-app.yml
new file mode 100644
index 0000000000..4fb8eec773
--- /dev/null
+++ b/.github/workflows/python-app.yml
@@ -0,0 +1,27 @@
+name: Python CI
+
+on: [push]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python 3.10
+      uses: actions/setup-python@v3
+      with:
+        python-version: "3.10"
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install flake8 pytest
+        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+    - name: Lint with flake8
+      run: |
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    - name: Test with pytest
+      run: |
+        pytest test_ml.py -v
diff --git a/local_api.py b/local_api.py
index a3bff2f988..da945cdb66 100644
--- a/local_api.py
+++ b/local_api.py
@@ -1,14 +1,14 @@
 import json
-
 import requests
 
-# TODO: send a GET using the URL http://127.0.0.1:8000
-r = None # Your code here
+# DONE: send a GET using the URL http://127.0.0.1:8000
+local_URL = "http://127.0.0.1:8000/"
+r = requests.get(local_URL)
 
-# TODO: print the status code
-# print()
-# TODO: print the welcome message
-# print()
+# DONE: print the status code
+print("Get request status code: ", r.status_code)
+# DONE?: print the welcome message
+print("Welcome Message:", r.json())
 
 
 
@@ -26,13 +26,17 @@
     "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()
+# DONE?: send a POST using the data above
+#r = requests.post(local_URL+"data?",
+#                         data = data)
+r2 = requests.post(f'{local_URL}data', json=data)
+
+
+# DONE: print the status code
+print("Post request status code: ", r2.status_code)
+# DONE: print the result
+print("Inference result: ", r2.json())
\ No newline at end of file
diff --git a/main.py b/main.py
index 638e2414de..a4217ff79a 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)
@@ -15,7 +16,9 @@ class Data(BaseModel):
     education: str = Field(..., example="HS-grad")
     education_num: int = Field(..., example=10, alias="education-num")
     marital_status: str = Field(
-        ..., example="Married-civ-spouse", alias="marital-status"
+        ...,
+        example="Married-civ-spouse",
+        alias="marital-status",
     )
     occupation: str = Field(..., example="Prof-specialty")
     relationship: str = Field(..., example="Husband")
@@ -24,26 +27,31 @@ class Data(BaseModel):
     capital_gain: int = Field(..., example=0, alias="capital-gain")
     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")
+    native_country: str = Field(
+        ...,
+        example="United-States",
+        alias="native-country",
+    )
+
+
+project_path = "/mnt/c/Users/kaleb/Desktop/DEPLOYING-A-SCALABLE-ML-PIPELINE-WITH-FASTAPI"
 
-path = None # TODO: enter the path for the saved encoder 
-encoder = load_model(path)
+encoder_path = os.path.join(project_path, "model", "encoder.pkl")
+encoder = load_model(encoder_path)
 
-path = None # TODO: enter the path for the saved model 
-model = load_model(path)
+model_path = os.path.join(project_path, "model", "model.pkl")
+model = load_model(model_path)
+
+
+app = FastAPI()
 
-# TODO: create a RESTful API using FastAPI
-app = None # your code here
 
-# TODO: 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! Welcome to Stephen's API."}
 
 
-# TODO: create a POST on a different path that does model inference
 @app.post("/data/")
 async def post_inference(data: Data):
     # DO NOT MODIFY: turn the Pydantic model into a dict.
@@ -64,11 +72,15 @@ async def post_inference(data: Data):
         "sex",
         "native-country",
     ]
+
     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
-    return {"result": apply_label(_inference)}
+
+    _inference = inference(model, data_processed)
+    return {
+        "result": apply_label(_inference)
+    }
diff --git a/ml/model.py b/ml/model.py
index f361110f18..32a211d21a 100644
--- a/ml/model.py
+++ b/ml/model.py
@@ -1,7 +1,8 @@
 import pickle
 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  # or your chosen model
+import numpy as np
 
 # Optional: implement hyperparameter tuning.
 def train_model(X_train, y_train):
@@ -20,7 +21,9 @@ def train_model(X_train, y_train):
         Trained machine learning model.
     """
     # TODO: implement the function
-    pass
+    model = RandomForestClassifier(random_state=42)
+    model.fit(X_train, y_train)
+    return model
 
 
 def compute_model_metrics(y, preds):
@@ -60,7 +63,7 @@ def inference(model, X):
         Predictions from the model.
     """
     # TODO: implement the function
-    pass
+    return model.predict(X)
 
 def save_model(model, path):
     """ Serializes model to a file.
@@ -73,12 +76,14 @@ def save_model(model, path):
         Path to save pickle 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
+    with open(path, 'rb') as f:
+        return pickle.load(f)
 
 
 def performance_on_categorical_slice(
@@ -117,12 +122,15 @@ def performance_on_categorical_slice(
     fbeta : float
 
     """
-    # TODO: implement the function
+    slice_data = 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
+        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..051c53f367
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..0bd32041a0
Binary files /dev/null and b/model/model.pkl differ
diff --git a/model_card.md b/model_card.md
new file mode 100644
index 0000000000..084b260f82
--- /dev/null
+++ b/model_card.md
@@ -0,0 +1,55 @@
+# Model Card
+
+For additional information see the Model Card paper: https://arxiv.org/pdf/1810.03993.pdf
+
+## Model Details
+
+- Developed by: Stephen Byrd, Feburary 2025
+- Model Type: This model uses a **Random Forest Classifier** for binary classification.
+- Dataset: The model is trained on the **Adult Census Dataset** from the UCI Machine Learning Repository.
+
+## Intended Use
+
+- The model is intended to predict whether an individual earns more than $50,000 per year based on demographic features from the census data.
+
+## Training Data
+
+- Dataset Source: The data is extracted from the 1994 Census database.
+- Features:
+  - age
+  - workclass
+  - education
+  - education-num
+  - marital-status
+  - occupation
+  - relationship
+  - race
+  - sex
+  - capital-gain
+  - capital-loss
+  - hours-per-week
+  - native-country
+- Target Label: 'salary' with values '>50K' (1) and '<=50K' (0).
+
+## Evaluation Data
+
+- Validation Method: The model is evaluated using a test dataset split from the original data.
+- Metrics Used: Precision, Recall, F1 Score.
+
+## Metrics
+
+- Metrics Used: Precision, Recall, F1 Score.
+- Model Performance:
+  - Precision: **0.7419**
+  - Recall: **0.6384**
+  - F1 Score: **0.6863**
+
+## Ethical Considerations
+
+- The dataset may have biases towards certain demographics (e.g., more men than women, predominantly white individuals).
+- No direct human life risks are associated with this model.
+
+## Caveats and Recommendations
+
+- Limitations: The model's performance could be improved with further tuning or using different classifiers.
+- Future Work: Consider using techniques like SHAP for feature importance analysis or exploring other classification models.
diff --git a/model_card.md:Zone.Identifier b/model_card.md:Zone.Identifier
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/model_card_template.md b/model_card_template.md
deleted file mode 100644
index 0392f3b9eb..0000000000
--- a/model_card_template.md
+++ /dev/null
@@ -1,18 +0,0 @@
-# Model Card
-
-For additional information see the Model Card paper: https://arxiv.org/pdf/1810.03993.pdf
-
-## Model Details
-
-## Intended Use
-
-## Training Data
-
-## Evaluation Data
-
-## Metrics
-_Please include the metrics used and your model's performance on those metrics._
-
-## Ethical Considerations
-
-## Caveats and Recommendations
diff --git a/screenshots/continuous_integration.png b/screenshots/continuous_integration.png
new file mode 100644
index 0000000000..38ffc8a1a2
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..a1b3d0c789
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..d56eda4430
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..8d94b84e6f
--- /dev/null
+++ b/slice_output.txt
@@ -0,0 +1,396 @@
+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
+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..158d6f62c9 100644
--- a/test_ml.py
+++ b/test_ml.py
@@ -1,28 +1,37 @@
 import pytest
-# TODO: add necessary import
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+from sklearn.datasets import make_classification
+from sklearn.ensemble import RandomForestClassifier
+from ml.model import train_model
 
-# TODO: implement the first test. Change the function name and input as needed
-def test_one():
+def test_load_data():
     """
-    # add description for the first test
+    This test verifies that the data is loaded correctly and has the expected shape.
     """
-    # Your code here
-    pass
+    X, y = make_classification(n_samples=100, n_features=8, random_state=42)
+    assert X.shape == (100, 8), f"Incorrect shape for X: {X.shape}"
+    assert y.shape == (100,), f"Incorrect shape for y: {y.shape}"
 
 
-# TODO: implement the second test. Change the function name and input as needed
-def test_two():
+def test_model_type():
     """
-    # add description for the second test
+    This test verifies that model returned is correct: RandomForestClassifier.
     """
-    # Your code here
-    pass
+    X, y = make_classification(n_samples=100, n_features=8, random_state=42)
+    model = train_model(X, y)
+    assert type(model) == RandomForestClassifier, f"Incorrect model type: {type(model)}"
 
 
-# TODO: implement the third test. Change the function name and input as needed
-def test_three():
+
+def test_preprocessing_scaler():
     """
-    # add description for the third test
+    This test ensures that the scaling of the data is done properly, an important step in preprocessing.
     """
-    # Your code here
-    pass
+    scaler = StandardScaler()
+    X, _ = make_classification(n_samples=100, n_features=8, random_state=42)
+    X_scaled = scaler.fit_transform(X)
+    
+    # Check that the mean and standard deviation are close to expected values
+    assert np.isclose(np.mean(X_scaled), 0), f"Incorrect mean: {np.mean(X_scaled)}"
+    assert np.isclose(np.std(X_scaled), 1), f"Incorrect std dev: {np.std(X_scaled)}"    
\ No newline at end of file
diff --git a/train_model.py b/train_model.py
index ae783ed5b9..8874195f0d 100644
--- a/train_model.py
+++ b/train_model.py
@@ -1,8 +1,6 @@
 import os
-
 import pandas as pd
 from sklearn.model_selection import train_test_split
-
 from ml.data import process_data
 from ml.model import (
     compute_model_metrics,
@@ -12,17 +10,14 @@
     save_model,
     train_model,
 )
-# TODO: load the cencus.csv data
-project_path = "Your path here"
-data_path = os.path.join(project_path, "data", "census.csv")
-print(data_path)
-data = None # your code here
 
-# 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
+# Load the census data
+data = pd.read_csv("data/census.csv")
 
-# DO NOT MODIFY
+# Split data into train and test sets
+train, test = train_test_split(data, test_size=0.20, random_state=42)
+
+# Define categorical features
 cat_features = [
     "workclass",
     "education",
@@ -34,14 +29,15 @@
     "native-country",
 ]
 
-# TODO: use the process_data function provided to process the data.
+# Process training 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
+)
 
+# Process test data
 X_test, y_test, _, _ = process_data(
     test,
     categorical_features=cat_features,
@@ -51,37 +47,42 @@
     lb=lb,
 )
 
-# TODO: use the train_model function to train the model on the training dataset
-model = None # your code here
+# Train model
+model = train_model(X_train, y_train)
 
-# save the model and the encoder
-model_path = os.path.join(project_path, "model", "model.pkl")
+# Save model and encoder
+model_path = os.path.join("model", "model.pkl")
 save_model(model, model_path)
-encoder_path = os.path.join(project_path, "model", "encoder.pkl")
+encoder_path = os.path.join("model", "encoder.pkl")
 save_model(encoder, encoder_path)
 
-# load the model
-model = load_model(
-    model_path
-) 
+# Load model for inference
+model = load_model(model_path)
 
-# TODO: use the inference function to run the model inferences on the test dataset.
-preds = None # your code here
+# Make predictions
+preds = inference(model, X_test)
 
-# 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}")
+# Calculate metrics
+precision, recall, fbeta = compute_model_metrics(y_test, preds)
+print(f"Precision: {precision:.4f} | Recall: {recall:.4f} | F1: {fbeta:.4f}")
 
-# TODO: compute the performance on model slices using the performance_on_categorical_slice function
-# iterate through the categorical features
+# Compute performance on slices
+# Compute performance on slices
 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
+        precision, recall, fbeta = performance_on_categorical_slice(
+            test, 
+            col, 
+            slicevalue, 
+            cat_features, 
+            "salary", 
+            encoder, 
+            lb, 
+            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: {precision:.4f} | Recall: {recall:.4f} | F1: {fbeta:.4f}", file=f)
+