diff --git a/.gitignore b/.gitignore
index e69de29..f561a7d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1,91 @@
+### Python ###
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# Virtual environment
+venv/
+.env/
+.venv/
+
+# Jupyter Notebook
+.ipynb_checkpoints/
+*.ipynb
+
+# VS Code
+.vscode/
+!.vscode/settings.json
+!.vscode/tasks.json
+!.vscode/launch.json
+!.vscode/extensions.json
+*.code-workspace
+
+# Data files
+data/raw/
+data/processed/
+*.csv
+*.pkl
+*.pickle
+*.h5
+*.hdf5
+*.feather
+*.parquet
+
+# Model files
+models/
+!models/README.md
+*.pkl
+*.pickle
+
+# Logs and databases
+*.log
+*.sqlite
+*.db
+
+# IDE
+.idea/
+.DS_Store
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
\ No newline at end of file
diff --git a/app.py b/app.py
new file mode 100644
index 0000000..06ff7fd
--- /dev/null
+++ b/app.py
@@ -0,0 +1,79 @@
+from flask import Flask, render_template, request, jsonify
+import joblib
+import pandas as pd
+import numpy as np
+from datetime import datetime
+
+app = Flask(__name__)
+
+# Load the model
+try:
+    model = joblib.load('models/fraud_model.pkl')
+except Exception as e:
+    print(f"Error loading model: {e}")
+    raise
+
+
+def preprocess_input(data):
+    # Convert to DataFrame
+    df = pd.DataFrame([data])
+
+    # Convert numeric fields explicitly
+    numeric_fields = ['amt', 'city_pop', 'lat', 'long', 'merch_lat', 'merch_long']
+    for field in numeric_fields:
+        df[field] = pd.to_numeric(df[field], errors='coerce')
+
+    # Convert transaction time to datetime
+    df['trans_date_trans_time'] = pd.to_datetime(df['trans_date_trans_time'])
+
+    # Extract time features
+    df['hour'] = df['trans_date_trans_time'].dt.hour
+    df['day_of_week'] = df['trans_date_trans_time'].dt.dayofweek
+    df['month'] = df['trans_date_trans_time'].dt.month
+
+    # Calculate age from dob
+    df['dob'] = pd.to_datetime(df['dob'])
+    df['age'] = (pd.to_datetime('today') - df['dob']).dt.days // 365
+
+    # Calculate distance between user and merchant
+    df['distance'] = np.sqrt(
+        (df['lat'].astype(float) - df['merch_lat'].astype(float))**2 + 
+        (df['long'].astype(float) - df['merch_long'].astype(float))**2
+    )
+
+    # Drop unnecessary columns
+    return df.drop(['trans_date_trans_time', 'dob'], axis=1, errors='ignore')
+
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    try:
+        # Get data from form
+        data = request.form.to_dict()
+
+        # Preprocess the input
+        processed_data = preprocess_input(data)
+
+        # Make prediction
+        prediction = model.predict(processed_data)
+        probability = model.predict_proba(processed_data)[0][1]
+
+        result = {
+            'prediction': int(prediction[0]),
+            'probability': float(probability),
+            'is_fraud': bool(prediction[0])
+        }
+
+        return render_template('index.html', prediction=result)
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 400
+
+
+if __name__ == '__main__':
+    app.run(debug=True)
diff --git a/data/raw/fraudTest.csv b/data/raw/fraudTest.csv
index bc5d7d2..d771459 100644
--- a/data/raw/fraudTest.csv
+++ b/data/raw/fraudTest.csv
@@ -1,5 +1,5 @@
 ,trans_date_trans_time,cc_num,merchant,category,amt,first,last,gender,street,city,state,zip,lat,long,city_pop,job,dob,trans_num,unix_time,merch_lat,merch_long,is_fraud
-0,2020-06-21 12:14:25,2291163933867244,fraud_Kirlin and Sons,personal_care,2.86,Jeff,Elliott,M,351 Darlene Green,Columbia,SC,29209,33.9659,-80.9355,333497,Mechanical engineer,1968-03-19,2da90c7d74bd46a0caf3777415b3ebd3,1371816865,33.986391,-81.200714,0
+0,2020-06-21 12:14:25,2291163933867244,fraud_Kirlin and Sons,personal_care,2.86,Jeff,Elliott,M,351 Darlene Green,Columbia,SC,29209,33.9659,-80.9355,333497,Mechanical engineer,1968-03-19,2da90c7d74bd46a0caf3777415b3ebd3,1371816865,,-81.2033.9863910714,0
 1,2020-06-21 12:14:33,3573030041201292,fraud_Sporer-Keebler,personal_care,29.84,Joanne,Williams,F,3638 Marsh Union,Altonah,UT,84002,40.3207,-110.436,302,"Sales professional, IT",1990-01-17,324cc204407e99f51b0d6ca0055005e7,1371816873,39.450497999999996,-109.960431,0
 2,2020-06-21 12:14:53,3598215285024754,"fraud_Swaniawski, Nitzsche and Welch",health_fitness,41.28,Ashley,Lopez,F,9333 Valentine Point,Bellmore,NY,11710,40.6729,-73.5365,34496,"Librarian, public",1970-10-21,c81755dbbbea9d5c77f094348a7579be,1371816893,40.49581,-74.196111,0
 3,2020-06-21 12:15:15,3591919803438423,fraud_Haley Group,misc_pos,60.05,Brian,Williams,M,32941 Krystal Mill Apt. 552,Titusville,FL,32780,28.5697,-80.8191,54767,Set designer,1987-07-25,2159175b9efe66dc301f149d3d5abf8c,1371816915,28.812397999999998,-80.883061,0
diff --git a/experiments/eda.ipynb b/experiments/eda.ipynb
index e69de29..4072d0b 100644
--- a/experiments/eda.ipynb
+++ b/experiments/eda.ipynb
@@ -0,0 +1,1037 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "v5Jd9WU1KIyi"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "from datetime import datetime\n",
+        "\n",
+        "# Load the data\n",
+        "df = pd.read_csv('data/raw/fraudTrain.csv')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 585
+        },
+        "id": "jYLv7ly6OJkC",
+        "outputId": "882d3f2a-45b7-4f41-c5eb-eb67a8dc80d8"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "First 5 Rows:\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-3fe63d94-9504-4e6d-bf14-127d836bf954\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Unnamed: 0</th>\n",
+              "      <th>trans_date_trans_time</th>\n",
+              "      <th>cc_num</th>\n",
+              "      <th>merchant</th>\n",
+              "      <th>category</th>\n",
+              "      <th>amt</th>\n",
+              "      <th>first</th>\n",
+              "      <th>last</th>\n",
+              "      <th>gender</th>\n",
+              "      <th>street</th>\n",
+              "      <th>...</th>\n",
+              "      <th>lat</th>\n",
+              "      <th>long</th>\n",
+              "      <th>city_pop</th>\n",
+              "      <th>job</th>\n",
+              "      <th>dob</th>\n",
+              "      <th>trans_num</th>\n",
+              "      <th>unix_time</th>\n",
+              "      <th>merch_lat</th>\n",
+              "      <th>merch_long</th>\n",
+              "      <th>is_fraud</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>2019-01-01 00:00:18</td>\n",
+              "      <td>2703186189652095</td>\n",
+              "      <td>fraud_Rippin, Kub and Mann</td>\n",
+              "      <td>misc_net</td>\n",
+              "      <td>4.97</td>\n",
+              "      <td>Jennifer</td>\n",
+              "      <td>Banks</td>\n",
+              "      <td>F</td>\n",
+              "      <td>561 Perry Cove</td>\n",
+              "      <td>...</td>\n",
+              "      <td>36.0788</td>\n",
+              "      <td>-81.1781</td>\n",
+              "      <td>3495</td>\n",
+              "      <td>Psychologist, counselling</td>\n",
+              "      <td>1988-03-09</td>\n",
+              "      <td>0b242abb623afc578575680df30655b9</td>\n",
+              "      <td>1325376018</td>\n",
+              "      <td>36.011293</td>\n",
+              "      <td>-82.048315</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>2019-01-01 00:00:44</td>\n",
+              "      <td>630423337322</td>\n",
+              "      <td>fraud_Heller, Gutmann and Zieme</td>\n",
+              "      <td>grocery_pos</td>\n",
+              "      <td>107.23</td>\n",
+              "      <td>Stephanie</td>\n",
+              "      <td>Gill</td>\n",
+              "      <td>F</td>\n",
+              "      <td>43039 Riley Greens Suite 393</td>\n",
+              "      <td>...</td>\n",
+              "      <td>48.8878</td>\n",
+              "      <td>-118.2105</td>\n",
+              "      <td>149</td>\n",
+              "      <td>Special educational needs teacher</td>\n",
+              "      <td>1978-06-21</td>\n",
+              "      <td>1f76529f8574734946361c461b024d99</td>\n",
+              "      <td>1325376044</td>\n",
+              "      <td>49.159047</td>\n",
+              "      <td>-118.186462</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2</td>\n",
+              "      <td>2019-01-01 00:00:51</td>\n",
+              "      <td>38859492057661</td>\n",
+              "      <td>fraud_Lind-Buckridge</td>\n",
+              "      <td>entertainment</td>\n",
+              "      <td>220.11</td>\n",
+              "      <td>Edward</td>\n",
+              "      <td>Sanchez</td>\n",
+              "      <td>M</td>\n",
+              "      <td>594 White Dale Suite 530</td>\n",
+              "      <td>...</td>\n",
+              "      <td>42.1808</td>\n",
+              "      <td>-112.2620</td>\n",
+              "      <td>4154</td>\n",
+              "      <td>Nature conservation officer</td>\n",
+              "      <td>1962-01-19</td>\n",
+              "      <td>a1a22d70485983eac12b5b88dad1cf95</td>\n",
+              "      <td>1325376051</td>\n",
+              "      <td>43.150704</td>\n",
+              "      <td>-112.154481</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3</td>\n",
+              "      <td>2019-01-01 00:01:16</td>\n",
+              "      <td>3534093764340240</td>\n",
+              "      <td>fraud_Kutch, Hermiston and Farrell</td>\n",
+              "      <td>gas_transport</td>\n",
+              "      <td>45.00</td>\n",
+              "      <td>Jeremy</td>\n",
+              "      <td>White</td>\n",
+              "      <td>M</td>\n",
+              "      <td>9443 Cynthia Court Apt. 038</td>\n",
+              "      <td>...</td>\n",
+              "      <td>46.2306</td>\n",
+              "      <td>-112.1138</td>\n",
+              "      <td>1939</td>\n",
+              "      <td>Patent attorney</td>\n",
+              "      <td>1967-01-12</td>\n",
+              "      <td>6b849c168bdad6f867558c3793159a81</td>\n",
+              "      <td>1325376076</td>\n",
+              "      <td>47.034331</td>\n",
+              "      <td>-112.561071</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>4</td>\n",
+              "      <td>2019-01-01 00:03:06</td>\n",
+              "      <td>375534208663984</td>\n",
+              "      <td>fraud_Keeling-Crist</td>\n",
+              "      <td>misc_pos</td>\n",
+              "      <td>41.96</td>\n",
+              "      <td>Tyler</td>\n",
+              "      <td>Garcia</td>\n",
+              "      <td>M</td>\n",
+              "      <td>408 Bradley Rest</td>\n",
+              "      <td>...</td>\n",
+              "      <td>38.4207</td>\n",
+              "      <td>-79.4629</td>\n",
+              "      <td>99</td>\n",
+              "      <td>Dance movement psychotherapist</td>\n",
+              "      <td>1986-03-28</td>\n",
+              "      <td>a41d7549acf90789359a9aa5346dcb46</td>\n",
+              "      <td>1325376186</td>\n",
+              "      <td>38.674999</td>\n",
+              "      <td>-78.632459</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 23 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3fe63d94-9504-4e6d-bf14-127d836bf954')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-3fe63d94-9504-4e6d-bf14-127d836bf954 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-3fe63d94-9504-4e6d-bf14-127d836bf954');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-c92967be-83e1-4f69-a63f-ea04d92f4d66\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c92967be-83e1-4f69-a63f-ea04d92f4d66')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-c92967be-83e1-4f69-a63f-ea04d92f4d66 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "   Unnamed: 0 trans_date_trans_time            cc_num  \\\n",
+              "0           0   2019-01-01 00:00:18  2703186189652095   \n",
+              "1           1   2019-01-01 00:00:44      630423337322   \n",
+              "2           2   2019-01-01 00:00:51    38859492057661   \n",
+              "3           3   2019-01-01 00:01:16  3534093764340240   \n",
+              "4           4   2019-01-01 00:03:06   375534208663984   \n",
+              "\n",
+              "                             merchant       category     amt      first  \\\n",
+              "0          fraud_Rippin, Kub and Mann       misc_net    4.97   Jennifer   \n",
+              "1     fraud_Heller, Gutmann and Zieme    grocery_pos  107.23  Stephanie   \n",
+              "2                fraud_Lind-Buckridge  entertainment  220.11     Edward   \n",
+              "3  fraud_Kutch, Hermiston and Farrell  gas_transport   45.00     Jeremy   \n",
+              "4                 fraud_Keeling-Crist       misc_pos   41.96      Tyler   \n",
+              "\n",
+              "      last gender                        street  ...      lat      long  \\\n",
+              "0    Banks      F                561 Perry Cove  ...  36.0788  -81.1781   \n",
+              "1     Gill      F  43039 Riley Greens Suite 393  ...  48.8878 -118.2105   \n",
+              "2  Sanchez      M      594 White Dale Suite 530  ...  42.1808 -112.2620   \n",
+              "3    White      M   9443 Cynthia Court Apt. 038  ...  46.2306 -112.1138   \n",
+              "4   Garcia      M              408 Bradley Rest  ...  38.4207  -79.4629   \n",
+              "\n",
+              "   city_pop                                job         dob  \\\n",
+              "0      3495          Psychologist, counselling  1988-03-09   \n",
+              "1       149  Special educational needs teacher  1978-06-21   \n",
+              "2      4154        Nature conservation officer  1962-01-19   \n",
+              "3      1939                    Patent attorney  1967-01-12   \n",
+              "4        99     Dance movement psychotherapist  1986-03-28   \n",
+              "\n",
+              "                          trans_num   unix_time  merch_lat  merch_long  \\\n",
+              "0  0b242abb623afc578575680df30655b9  1325376018  36.011293  -82.048315   \n",
+              "1  1f76529f8574734946361c461b024d99  1325376044  49.159047 -118.186462   \n",
+              "2  a1a22d70485983eac12b5b88dad1cf95  1325376051  43.150704 -112.154481   \n",
+              "3  6b849c168bdad6f867558c3793159a81  1325376076  47.034331 -112.561071   \n",
+              "4  a41d7549acf90789359a9aa5346dcb46  1325376186  38.674999  -78.632459   \n",
+              "\n",
+              "   is_fraud  \n",
+              "0         0  \n",
+              "1         0  \n",
+              "2         0  \n",
+              "3         0  \n",
+              "4         0  \n",
+              "\n",
+              "[5 rows x 23 columns]"
+            ]
+          },
+          "execution_count": 30,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "print(\"\\nFirst 5 Rows:\")\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DDaHkHmYMuFd",
+        "outputId": "993fc538-4afc-4cd3-d164-33c30927adba"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Dataset Shape: (1296675, 23)\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(f\"Dataset Shape: {df.shape}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Lg3CbomiOS43",
+        "outputId": "ee426872-1069-4987-a774-dfced72f2ddc"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Data Types:\n",
+            "Unnamed: 0                 int64\n",
+            "trans_date_trans_time     object\n",
+            "cc_num                     int64\n",
+            "merchant                  object\n",
+            "category                  object\n",
+            "amt                      float64\n",
+            "first                     object\n",
+            "last                      object\n",
+            "gender                    object\n",
+            "street                    object\n",
+            "city                      object\n",
+            "state                     object\n",
+            "zip                        int64\n",
+            "lat                      float64\n",
+            "long                     float64\n",
+            "city_pop                   int64\n",
+            "job                       object\n",
+            "dob                       object\n",
+            "trans_num                 object\n",
+            "unix_time                  int64\n",
+            "merch_lat                float64\n",
+            "merch_long               float64\n",
+            "is_fraud                   int64\n",
+            "dtype: object\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"\\nData Types:\")\n",
+        "print(df.dtypes)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "hgnd0URvOZnt",
+        "outputId": "f97be22a-d45e-43c4-8c07-09271a88ee32"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Missing Values:\n",
+            "Unnamed: 0               0\n",
+            "trans_date_trans_time    0\n",
+            "cc_num                   0\n",
+            "merchant                 0\n",
+            "category                 0\n",
+            "amt                      0\n",
+            "first                    0\n",
+            "last                     0\n",
+            "gender                   0\n",
+            "street                   0\n",
+            "city                     0\n",
+            "state                    0\n",
+            "zip                      0\n",
+            "lat                      0\n",
+            "long                     0\n",
+            "city_pop                 0\n",
+            "job                      0\n",
+            "dob                      0\n",
+            "trans_num                0\n",
+            "unix_time                0\n",
+            "merch_lat                0\n",
+            "merch_long               0\n",
+            "is_fraud                 0\n",
+            "dtype: int64\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"\\nMissing Values:\")\n",
+        "print(df.isnull().sum())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "doXJcfKcMK7n",
+        "outputId": "6b07061b-a37b-48bd-b199-a43711ce1805"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Fraud Distribution:\n",
+            "is_fraud\n",
+            "0    99.421135\n",
+            "1     0.578865\n",
+            "Name: proportion, dtype: float64\n"
+          ]
+        }
+      ],
+      "source": [
+        "fraud_percentage = df['is_fraud'].value_counts(normalize=True) * 100\n",
+        "print(f\"Fraud Distribution:\\n{fraud_percentage}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 487
+        },
+        "id": "H1lvxe0GPlRj",
+        "outputId": "cbd5c9f1-6f33-4137-d3cd-a393548246e7"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(8,5))\n",
+        "sns.countplot(x='is_fraud', data=df)\n",
+        "plt.title('Fraud vs Non-Fraud Transactions')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 55,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 667
+        },
+        "id": "T1Rcu74DMH2F",
+        "outputId": "88834057-4cc9-4da6-91d9-fc5b16665d89"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "TRANSACTION AMOUNT ANALYSIS\n",
+            "count    1.296675e+06\n",
+            "mean     7.035104e+01\n",
+            "std      1.603160e+02\n",
+            "min      1.000000e+00\n",
+            "25%      9.650000e+00\n",
+            "50%      4.752000e+01\n",
+            "75%      8.314000e+01\n",
+            "max      2.894890e+04\n",
+            "Name: amt, dtype: float64\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1200x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Transaction Amount Analysis\n",
+        "print(\"TRANSACTION AMOUNT ANALYSIS\")\n",
+        "print(df['amt'].describe())\n",
+        "\n",
+        "plt.figure(figsize=(12,5))\n",
+        "plt.subplot(1,2,1)\n",
+        "sns.boxplot(x='is_fraud', y='amt', data=df)\n",
+        "plt.title('Transaction Amount by Fraud Status')\n",
+        "\n",
+        "\n",
+        "plt.subplot(1,2,2)\n",
+        "sns.histplot(data=df, x='amt', hue='is_fraud', bins=50, kde=True, element='step')\n",
+        "plt.title('Amount Distribution')\n",
+        "plt.xlim(0, 1000)\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 786
+        },
+        "id": "4CZPMlYjMBuA",
+        "outputId": "a43908c4-a037-48ed-de6a-848d946cc52f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1500x1000 with 3 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Time-Based Analysis\n",
+        "df['trans_date_trans_time'] = pd.to_datetime(df['trans_date_trans_time'])\n",
+        "df['hour'] = df['trans_date_trans_time'].dt.hour\n",
+        "df['day_of_week'] = df['trans_date_trans_time'].dt.dayofweek\n",
+        "df['month'] = df['trans_date_trans_time'].dt.month\n",
+        "\n",
+        "plt.figure(figsize=(15,10))\n",
+        "\n",
+        "plt.subplot(3,1,1)\n",
+        "sns.countplot(x='hour', hue='is_fraud', data=df)\n",
+        "plt.title('Transactions by Hour of Day')\n",
+        "\n",
+        "plt.subplot(3,1,2)\n",
+        "sns.countplot(x='day_of_week', hue='is_fraud', data=df)\n",
+        "plt.title('Transactions by Day of Week')\n",
+        "\n",
+        "plt.subplot(3,1,3)\n",
+        "sns.countplot(x='month', hue='is_fraud', data=df)\n",
+        "plt.title('Transactions by Month')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 667
+        },
+        "id": "h7h0O8FWMBb3",
+        "outputId": "ecda1612-7a93-4bd2-d19c-f79c6326feea"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "CATEGORY ANALYSIS\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1200x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Category Analysis\n",
+        "print(\"CATEGORY ANALYSIS\")\n",
+        "plt.figure(figsize=(12,6))\n",
+        "category_fraud = df.groupby('category')['is_fraud'].mean().sort_values(ascending=False)\n",
+        "category_fraud.plot(kind='bar')\n",
+        "plt.title('Fraud Percentage by Category')\n",
+        "plt.ylabel('Fraud Percentage')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 47,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 511
+        },
+        "id": "y1jJD0sEL_Ji",
+        "outputId": "95543981-d7ba-4804-a429-435a7c21c601"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "GEOGRAPHIC ANALYSIS\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1200x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Geographic Analysis\n",
+        "print(\"GEOGRAPHIC ANALYSIS\")\n",
+        "df['distance'] = np.sqrt((df['lat']-df['merch_lat'])**2 + (df['long']-df['merch_long'])**2)\n",
+        "\n",
+        "plt.figure(figsize=(12,5))\n",
+        "plt.subplot(1,2,1)\n",
+        "sns.boxplot(x='is_fraud', y='distance', data=df)\n",
+        "plt.title('Transaction Distance by Fraud Status')\n",
+        "\n",
+        "plt.subplot(1,2,2)\n",
+        "fraud_by_state = df[df['is_fraud']==1]['state'].value_counts().head(10)\n",
+        "fraud_by_state.plot(kind='bar')\n",
+        "plt.title('Top 10 States with Most Fraud')\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 48,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 511
+        },
+        "id": "tRqMouOAL73Z",
+        "outputId": "b4a18c45-1518-4509-b6c8-71bbf758a230"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "DEMOGRAPHIC ANALYSIS\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x500 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Demographic Analysis\n",
+        "print(\"DEMOGRAPHIC ANALYSIS\")\n",
+        "df['dob'] = pd.to_datetime(df['dob'])\n",
+        "df['age'] = (pd.to_datetime('today') - df['dob']).dt.days // 365\n",
+        "\n",
+        "plt.figure(figsize=(12,5))\n",
+        "plt.subplot(1,2,1)\n",
+        "sns.boxplot(x='is_fraud', y='age', data=df)\n",
+        "plt.title('Age Distribution by Fraud Status')\n",
+        "\n",
+        "plt.subplot(1,2,2)\n",
+        "sns.countplot(x='gender', hue='is_fraud', data=df)\n",
+        "plt.title('Fraud by Gender')\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 716
+        },
+        "id": "1oP9bUGwL4tg",
+        "outputId": "78767681-958e-4b4e-9906-b07051a62502"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "CORRELATION ANALYSIS\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x800 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Correlation Analysis\n",
+        "print(\"CORRELATION ANALYSIS\")\n",
+        "numeric_cols = ['amt', 'city_pop', 'distance', 'age']\n",
+        "corr_matrix = df[numeric_cols + ['is_fraud']].corr()\n",
+        "\n",
+        "plt.figure(figsize=(10,8))\n",
+        "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', center=0)\n",
+        "plt.title('Correlation Matrix')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 51,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "Kqxc67LYL1K_",
+        "outputId": "b1a984e1-3fd2-4f46-c26d-d8082d975884"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1400x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Geographic Distribution of Fraud\n",
+        "plt.figure(figsize=(14,6))\n",
+        "sns.scatterplot(x='long', y='lat', hue='is_fraud', data=df.sample(10000), alpha=0.6)\n",
+        "plt.title('Geographic Distribution of Fraud (Sample)')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 54,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ljcm6jdXLvF5",
+        "outputId": "7039b12c-99ca-452f-cfe2-52f5dfaca305"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "KEY FINDINGS SUMMARY\n",
+            "1. Fraud Rate: 0.58% of transactions are fraudulent\n",
+            "2. Fraud Amount: Median fraud amount is $396.50\n",
+            "3. Risky Hours: Highest fraud occurs between 22:00-23:00\n",
+            "4. Risky Categories: Top 3 risky categories - ['shopping_net', 'misc_net', 'grocery_pos']\n",
+            "5. Distance Impact: Median distance for fraud is 0.80 vs 0.80 for legit transactions\n",
+            "6. Age Impact: Median age for fraud is 53.00 vs 49.00 for legit transactions\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Key Findings Summary\n",
+        "print(\"KEY FINDINGS SUMMARY\")\n",
+        "print(f\"1. Fraud Rate: {fraud_percentage[1]:.2f}% of transactions are fraudulent\")\n",
+        "print(f\"2. Fraud Amount: Median fraud amount is ${df[df['is_fraud']==1]['amt'].median():.2f}\")\n",
+        "print(f\"3. Risky Hours: Highest fraud occurs between {df.groupby('hour')['is_fraud'].mean().idxmax()}:00-{df.groupby('hour')['is_fraud'].mean().idxmax()+1}:00\")\n",
+        "print(f\"4. Risky Categories: Top 3 risky categories - {category_fraud.head(3).index.tolist()}\")\n",
+        "print(f\"5. Distance Impact: Median distance for fraud is {df[df['is_fraud']==1]['distance'].median():.2f} vs {df[df['is_fraud']==0]['distance'].median():.2f} for legit transactions\")\n",
+        "print(f\"6. Age Impact: Median age for fraud is {df[df['is_fraud']==1]['age'].median():.2f} vs {df[df['is_fraud']==0]['age'].median():.2f} for legit transactions\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NaDQni_yLwoo"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/experiments/feature_enginerring.ipynb b/experiments/feature_enginerring.ipynb
deleted file mode 100644
index e69de29..0000000
diff --git a/experiments/model_training.ipynb b/experiments/model_training.ipynb
index e69de29..2e7a33a 100644
--- a/experiments/model_training.ipynb
+++ b/experiments/model_training.ipynb
@@ -0,0 +1,141 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "from sklearn.metrics import classification_report\n",
+        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
+        "from sklearn.compose import ColumnTransformer\n",
+        "from sklearn.pipeline import Pipeline\n",
+        "import joblib"
+      ],
+      "metadata": {
+        "id": "a1U4QR1AOKAK"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Load processed data\n",
+        "train_df = pd.read_csv('train_processed.csv')\n",
+        "test_df = pd.read_csv('test_processed.csv')"
+      ],
+      "metadata": {
+        "id": "mOjH5CqhOlTp"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Prepare features and target\n",
+        "X_train = train_df.drop(['is_fraud', 'trans_date_trans_time', 'trans_num', 'dob', 'unix_time'], axis=1)\n",
+        "y_train = train_df['is_fraud']\n",
+        "\n",
+        "X_test = test_df.drop(['is_fraud', 'trans_date_trans_time', 'trans_num', 'dob', 'unix_time'], axis=1)\n",
+        "y_test = test_df['is_fraud']\n"
+      ],
+      "metadata": {
+        "id": "VW8cpQ6FNvC9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define preprocessing\n",
+        "numeric_features = ['amt', 'city_pop', 'hour', 'day_of_week', 'month', 'age', 'distance']\n",
+        "numeric_transformer = StandardScaler()\n",
+        "\n",
+        "categorical_features = ['category', 'gender', 'job', 'merchant']\n",
+        "categorical_transformer = OneHotEncoder(handle_unknown='ignore')\n",
+        "\n",
+        "preprocessor = ColumnTransformer(\n",
+        "    transformers=[\n",
+        "        ('num', numeric_transformer, numeric_features),\n",
+        "        ('cat', categorical_transformer, categorical_features)\n",
+        "    ])"
+      ],
+      "metadata": {
+        "id": "GkGMEEYqOc_p"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create pipeline\n",
+        "model = Pipeline(steps=[\n",
+        "    ('preprocessor', preprocessor),\n",
+        "    ('classifier', RandomForestClassifier(class_weight='balanced', random_state=42))\n",
+        "])\n"
+      ],
+      "metadata": {
+        "id": "s2VyUaV6OZw9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Train model\n",
+        "model.fit(X_train, y_train)"
+      ],
+      "metadata": {
+        "id": "6dXK4nJnOW40"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Evaluate on test data\n",
+        "y_pred = model.predict(X_test)\n",
+        "print(\"Test Set Performance:\")\n",
+        "print(classification_report(y_test, y_pred))"
+      ],
+      "metadata": {
+        "id": "PNV9fZhkOVFM"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Save model\n",
+        "joblib.dump(model, 'fraud_model.pkl')\n",
+        "print(\"Model saved to fraud_model.pkl\")"
+      ],
+      "metadata": {
+        "id": "4j2mlzRINc_N"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index e69de29..0cead16 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -0,0 +1,6 @@
+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+flask
+matplotlib
+joblib==1.5.1
diff --git a/static/style.css b/static/style.css
new file mode 100644
index 0000000..396ae72
--- /dev/null
+++ b/static/style.css
@@ -0,0 +1,71 @@
+body {
+    font-family: Arial, sans-serif;
+    line-height: 1.6;
+    margin: 0;
+    padding: 20px;
+    background-color: #f5f5f5;
+}
+
+.container {
+    max-width: 800px;
+    margin: 0 auto;
+    background: white;
+    padding: 20px;
+    border-radius: 8px;
+    box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
+}
+
+h1 {
+    text-align: center;
+    color: #333;
+}
+
+.form-group {
+    margin-bottom: 15px;
+}
+
+label {
+    display: block;
+    margin-bottom: 5px;
+    font-weight: bold;
+}
+
+input, select {
+    width: 100%;
+    padding: 8px;
+    border: 1px solid #ddd;
+    border-radius: 4px;
+    box-sizing: border-box;
+}
+
+button {
+    background-color: #4CAF50;
+    color: white;
+    padding: 10px 15px;
+    border: none;
+    border-radius: 4px;
+    cursor: pointer;
+    font-size: 16px;
+    width: 100%;
+}
+
+button:hover {
+    background-color: #45a049;
+}
+
+.result {
+    margin-top: 20px;
+    padding: 15px;
+    border-radius: 4px;
+    background-color: #f9f9f9;
+}
+
+.fraud {
+    color: #d32f2f;
+    font-weight: bold;
+}
+
+.legit {
+    color: #388e3c;
+    font-weight: bold;
+}
\ No newline at end of file
diff --git a/templates/index.html b/templates/index.html
new file mode 100644
index 0000000..bc82a45
--- /dev/null
+++ b/templates/index.html
@@ -0,0 +1,108 @@
+<!DOCTYPE html>
+<html>
+<head>
+    <title>Fraud Detection System</title>
+    <link rel="stylesheet" href="/static/style.css">
+</head>
+<body>
+    <div class="container">
+        <h1>Fraud Detection System</h1>
+        <form action="/predict" method="post">
+            <div class="form-group">
+                <label>Transaction Amount:</label>
+                <input type="number" step="0.01" name="amt" required>
+            </div>
+            
+            <div class="form-group">
+                <label>Category:</label>
+                <select name="category" required>
+                    <option value="entertainment">Entertainment</option>
+                    <option value="food_dining">Food & Dining</option>
+                    <option value="gas_transport">Gas & Transport</option>
+                    <option value="grocery_net">Grocery</option>
+                    <option value="grocery_pos">Grocery POS</option>
+                    <option value="health_fitness">Health & Fitness</option>
+                    <option value="home">Home</option>
+                    <option value="kids_pets">Kids & Pets</option>
+                    <option value="misc_net">Misc Net</option>
+                    <option value="misc_pos">Misc POS</option>
+                    <option value="personal_care">Personal Care</option>
+                    <option value="shopping_net">Shopping Net</option>
+                    <option value="shopping_pos">Shopping POS</option>
+                    <option value="travel">Travel</option>
+                </select>
+            </div>
+            
+            <div class="form-group">
+                <label>Gender:</label>
+                <select name="gender" required>
+                    <option value="M">Male</option>
+                    <option value="F">Female</option>
+                </select>
+            </div>
+            
+            <div class="form-group">
+                <label>Date of Birth (YYYY-MM-DD):</label>
+                <input type="text" name="dob" placeholder="1990-01-01" required>
+            </div>
+            
+            <div class="form-group">
+                <label>Transaction Time (YYYY-MM-DD HH:MM:SS):</label>
+                <input type="text" name="trans_date_trans_time" placeholder="2023-01-01 12:00:00" required>
+            </div>
+            
+            <div class="form-group">
+                <label>City Population:</label>
+                <input type="number" name="city_pop" required>
+            </div>
+            
+            <div class="form-group">
+                <label>Job:</label>
+                <input type="text" name="job" required>
+            </div>
+            
+            <div class="form-group">
+                <label>Merchant:</label>
+                <input type="text" name="merchant" required>
+            </div>
+            
+            <div class="form-group">
+                <label>User Latitude:</label>
+                <input type="number" step="0.000001" name="lat" required>
+            </div>
+            
+            <div class="form-group">
+                <label>User Longitude:</label>
+                <input type="number" step="0.000001" name="long" required>
+            </div>
+            
+            <div class="form-group">
+                <label>Merchant Latitude:</label>
+                <input type="number" step="0.000001" name="merch_lat" required>
+            </div>
+            
+            <div class="form-group">
+                <label>Merchant Longitude:</label>
+                <input type="number" step="0.000001" name="merch_long" required>
+            </div>
+            
+            <button type="submit">Check for Fraud</button>
+        </form>
+        
+        {% if prediction %}
+        <div class="result">
+            <h2>Prediction Result</h2>
+            <p><strong>Fraudulent:</strong> {{ prediction.is_fraud }}</p>
+            <p><strong>Probability:</strong> {{ "%.2f"|format(prediction.probability * 100) }}%</p>
+            <p class="{% if prediction.is_fraud %}fraud{% else %}legit{% endif %}">
+                {% if prediction.is_fraud %}
+                ⚠️ This transaction appears to be fraudulent!
+                {% else %}
+                ✓ This transaction appears to be legitimate.
+                {% endif %}
+            </p>
+        </div>
+        {% endif %}
+    </div>
+</body>
+</html>
\ No newline at end of file
diff --git a/train_model.py b/train_model.py
new file mode 100644
index 0000000..398ce71
--- /dev/null
+++ b/train_model.py
@@ -0,0 +1,100 @@
+import pandas as pd
+import numpy as np
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import classification_report
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+import joblib
+from datetime import datetime
+
+
+def load_data(train_file, test_file):
+    train_df = pd.read_csv(train_file)
+    test_df = pd.read_csv(test_file)
+
+    # Drop rows with missing values
+    train_df.dropna(inplace=True)
+    test_df.dropna(inplace=True)
+
+    return train_df, test_df
+
+
+def feature_engineering(df):
+    # Convert transaction time to datetime
+    df['trans_date_trans_time'] = pd.to_datetime(df['trans_date_trans_time'])
+
+    # Extract time features
+    df['hour'] = df['trans_date_trans_time'].dt.hour
+    df['day_of_week'] = df['trans_date_trans_time'].dt.dayofweek
+    df['month'] = df['trans_date_trans_time'].dt.month
+
+    # Calculate age from dob
+    df['dob'] = pd.to_datetime(df['dob'])
+    df['age'] = (pd.to_datetime('today') - df['dob']).dt.days // 365
+
+    # Calculate distance between user and merchant
+    df['distance'] = np.sqrt((df['lat']-df['merch_lat'])**2 + (df['long']-df['merch_long'])**2)
+
+    return df
+
+
+def train_model(train_df, test_df):
+    # Define features and target
+    X_train = train_df.drop(['is_fraud', 'trans_date_trans_time', 'trans_num', 'dob', 'unix_time'], axis=1)
+    y_train = train_df['is_fraud']
+
+    X_test = test_df.drop(['is_fraud', 'trans_date_trans_time', 'trans_num', 'dob', 'unix_time'], axis=1)
+    y_test = test_df['is_fraud']
+
+    # Define preprocessing
+    numeric_features = ['amt', 'city_pop', 'hour', 'day_of_week', 'month', 'age', 'distance']
+    numeric_transformer = StandardScaler()
+
+    categorical_features = ['category', 'gender', 'job', 'merchant']
+    categorical_transformer = OneHotEncoder(handle_unknown='ignore')
+
+    preprocessor = ColumnTransformer(
+        transformers=[
+            ('num', numeric_transformer, numeric_features),
+            ('cat', categorical_transformer, categorical_features)
+        ])
+
+    # Create pipeline
+    model = Pipeline(steps=[
+        ('preprocessor', preprocessor),
+        ('classifier', RandomForestClassifier(class_weight='balanced', random_state=42))
+    ])
+
+    # Train model
+    model.fit(X_train, y_train)
+
+    # Evaluate on test data
+    y_pred = model.predict(X_test)
+    print("Test Set Performance:")
+    print(classification_report(y_test, y_pred))
+
+    return model
+
+
+def main():
+    # Load data
+    train_df, test_df = load_data('data/raw/fraudTrain.csv', 'data/raw/fraudTest.csv')
+
+    # Feature engineering
+    train_df = feature_engineering(train_df)
+    test_df = feature_engineering(test_df)
+
+    # Print dataset sizes after cleaning
+    print(f"Training samples after cleaning: {len(train_df)}")
+    print(f"Test samples after cleaning: {len(test_df)}")
+
+    # Train model
+    model = train_model(train_df, test_df)
+
+    # Save model
+    joblib.dump(model, 'models/fraud_model.pkl')
+    print("Model saved to models/fraud_model.pkl")
+
+if __name__ == "__main__":
+    main()