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boladeE a70c0802ae Update project structure and configuration 
- Enhanced .gitignore to include additional Python-related files.
- Updated docker-compose.yml to version 3.8, added container name, health check, and network configuration.
- Modified Dockerfile to set environment variables, install system dependencies, and add a health check.
- Updated requirements.txt with specific package versions.
- Removed obsolete files related to cloud deployment and experiments.
- Improved EDA notebook with additional data loading and exploration steps.
2025-04-25 17:53:04 +01:00

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{
"cells": [
{
"cell_type": "markdown",
"id": "7f073598",
"metadata": {},
"source": [
"# Feature Engineering and Feature Selection for Fraud Detection"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1816729a",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
"from sklearn.feature_selection import VarianceThreshold, RFE\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.model_selection import train_test_split\n",
"from imblearn.under_sampling import RandomUnderSampler"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5309a15c",
"metadata": {},
"outputs": [],
"source": [
"df_train = pd.read_csv('../data/raw/fraudTrain.csv')\n",
"df_test = pd.read_csv('../data/raw/fraudTest.csv')\n",
"\n",
"df = pd.concat([df_train, df_test])\n",
"X = df.drop(['is_fraud'], axis=1)\n",
"y = df['is_fraud']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dd2b4e07",
"metadata": {},
"outputs": [],
"source": [
"rus = RandomUnderSampler(sampling_strategy=1)\n",
"X_res, y_res = rus.fit_resample(X, y)\n",
"\n",
"# Combine X_res and y_res into a single DataFrame\n",
"resampled_df = pd.concat([X_res, y_res], axis=1)\n",
"\n",
"# Save the combined DataFrame to a new file\n",
"resampled_df.to_csv('../data/processed/resampled_data.csv', index=False)\n",
"\n",
"ax = y_res.value_counts().plot.pie(autopct='%.2f')\n",
"_ = ax.set_title(\"Under-sampling\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "890be8a6",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<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>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>city</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>2020-12-15 18:33:17</td>\n",
" <td>30551643947183</td>\n",
" <td>fraud_Dickinson Ltd</td>\n",
" <td>personal_care</td>\n",
" <td>6.15</td>\n",
" <td>Morgan</td>\n",
" <td>Smith</td>\n",
" <td>F</td>\n",
" <td>1441 Bradley Place</td>\n",
" <td>Grover</td>\n",
" <td>...</td>\n",
" <td>35.1836</td>\n",
" <td>-81.4552</td>\n",
" <td>5621</td>\n",
" <td>Toxicologist</td>\n",
" <td>1973-11-14</td>\n",
" <td>7cab35c172dc6c78f551e981fe426fc6</td>\n",
" <td>1387132397</td>\n",
" <td>35.292860</td>\n",
" <td>-81.937193</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2019-12-23 08:14:17</td>\n",
" <td>5359543825610251</td>\n",
" <td>fraud_Kilback LLC</td>\n",
" <td>grocery_pos</td>\n",
" <td>67.35</td>\n",
" <td>Michael</td>\n",
" <td>Francis</td>\n",
" <td>M</td>\n",
" <td>1833 Jeanette Stravenue</td>\n",
" <td>Belgrade</td>\n",
" <td>...</td>\n",
" <td>45.7801</td>\n",
" <td>-111.1439</td>\n",
" <td>18182</td>\n",
" <td>Engineer, drilling</td>\n",
" <td>1975-06-29</td>\n",
" <td>ea272415875381d29cf29aee11550672</td>\n",
" <td>1356250457</td>\n",
" <td>46.228116</td>\n",
" <td>-111.718928</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2019-07-22 01:42:56</td>\n",
" <td>3560318482131952</td>\n",
" <td>fraud_Lehner, Mosciski and King</td>\n",
" <td>misc_net</td>\n",
" <td>1342.69</td>\n",
" <td>William</td>\n",
" <td>Skinner</td>\n",
" <td>M</td>\n",
" <td>524 Wu Spurs Suite 894</td>\n",
" <td>Mount Hope</td>\n",
" <td>...</td>\n",
" <td>34.4793</td>\n",
" <td>-87.4769</td>\n",
" <td>1312</td>\n",
" <td>Librarian, academic</td>\n",
" <td>1955-02-01</td>\n",
" <td>964730a13ec2011d75a3db37c55e526d</td>\n",
" <td>1342921376</td>\n",
" <td>34.898759</td>\n",
" <td>-88.125374</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2019-08-05 15:21:55</td>\n",
" <td>4477156602511939689</td>\n",
" <td>fraud_Medhurst, Cartwright and Ebert</td>\n",
" <td>personal_care</td>\n",
" <td>28.45</td>\n",
" <td>Angela</td>\n",
" <td>Ross</td>\n",
" <td>F</td>\n",
" <td>0107 Clements Point</td>\n",
" <td>American Fork</td>\n",
" <td>...</td>\n",
" <td>40.3928</td>\n",
" <td>-111.7941</td>\n",
" <td>42384</td>\n",
" <td>Futures trader</td>\n",
" <td>1992-12-29</td>\n",
" <td>518f49a0d3106c3f538c2f70d2f12e8a</td>\n",
" <td>1344180115</td>\n",
" <td>41.170642</td>\n",
" <td>-111.052342</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2020-01-27 20:49:30</td>\n",
" <td>30175986190993</td>\n",
" <td>fraud_Barton LLC</td>\n",
" <td>kids_pets</td>\n",
" <td>45.49</td>\n",
" <td>Rebecca</td>\n",
" <td>Butler</td>\n",
" <td>F</td>\n",
" <td>0665 Lisa Alley</td>\n",
" <td>Winger</td>\n",
" <td>...</td>\n",
" <td>47.5375</td>\n",
" <td>-95.9941</td>\n",
" <td>516</td>\n",
" <td>Applications developer</td>\n",
" <td>1966-06-07</td>\n",
" <td>8979eac3cefcacee095981dc43cd125d</td>\n",
" <td>1359319770</td>\n",
" <td>47.489127</td>\n",
" <td>-95.926267</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 22 columns</p>\n",
"</div>"
],
"text/plain": [
" trans_date_trans_time cc_num \\\n",
"0 2020-12-15 18:33:17 30551643947183 \n",
"1 2019-12-23 08:14:17 5359543825610251 \n",
"2 2019-07-22 01:42:56 3560318482131952 \n",
"3 2019-08-05 15:21:55 4477156602511939689 \n",
"4 2020-01-27 20:49:30 30175986190993 \n",
"\n",
" merchant category amt first \\\n",
"0 fraud_Dickinson Ltd personal_care 6.15 Morgan \n",
"1 fraud_Kilback LLC grocery_pos 67.35 Michael \n",
"2 fraud_Lehner, Mosciski and King misc_net 1342.69 William \n",
"3 fraud_Medhurst, Cartwright and Ebert personal_care 28.45 Angela \n",
"4 fraud_Barton LLC kids_pets 45.49 Rebecca \n",
"\n",
" last gender street city ... lat \\\n",
"0 Smith F 1441 Bradley Place Grover ... 35.1836 \n",
"1 Francis M 1833 Jeanette Stravenue Belgrade ... 45.7801 \n",
"2 Skinner M 524 Wu Spurs Suite 894 Mount Hope ... 34.4793 \n",
"3 Ross F 0107 Clements Point American Fork ... 40.3928 \n",
"4 Butler F 0665 Lisa Alley Winger ... 47.5375 \n",
"\n",
" long city_pop job dob \\\n",
"0 -81.4552 5621 Toxicologist 1973-11-14 \n",
"1 -111.1439 18182 Engineer, drilling 1975-06-29 \n",
"2 -87.4769 1312 Librarian, academic 1955-02-01 \n",
"3 -111.7941 42384 Futures trader 1992-12-29 \n",
"4 -95.9941 516 Applications developer 1966-06-07 \n",
"\n",
" trans_num unix_time merch_lat merch_long \\\n",
"0 7cab35c172dc6c78f551e981fe426fc6 1387132397 35.292860 -81.937193 \n",
"1 ea272415875381d29cf29aee11550672 1356250457 46.228116 -111.718928 \n",
"2 964730a13ec2011d75a3db37c55e526d 1342921376 34.898759 -88.125374 \n",
"3 518f49a0d3106c3f538c2f70d2f12e8a 1344180115 41.170642 -111.052342 \n",
"4 8979eac3cefcacee095981dc43cd125d 1359319770 47.489127 -95.926267 \n",
"\n",
" is_fraud \n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
"[5 rows x 22 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"\n",
"# Load data\n",
"df = resampled_df\n",
"df.drop(columns=['Unnamed: 0'], inplace=True)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "e798580f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 19302 entries, 0 to 19301\n",
"Data columns (total 22 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 trans_date_trans_time 19302 non-null object \n",
" 1 cc_num 19302 non-null int64 \n",
" 2 merchant 19302 non-null object \n",
" 3 category 19302 non-null object \n",
" 4 amt 19302 non-null float64\n",
" 5 first 19302 non-null object \n",
" 6 last 19302 non-null object \n",
" 7 gender 19302 non-null object \n",
" 8 street 19302 non-null object \n",
" 9 city 19302 non-null object \n",
" 10 state 19302 non-null object \n",
" 11 zip 19302 non-null int64 \n",
" 12 lat 19302 non-null float64\n",
" 13 long 19302 non-null float64\n",
" 14 city_pop 19302 non-null int64 \n",
" 15 job 19302 non-null object \n",
" 16 dob 19302 non-null object \n",
" 17 trans_num 19302 non-null object \n",
" 18 unix_time 19302 non-null int64 \n",
" 19 merch_lat 19302 non-null float64\n",
" 20 merch_long 19302 non-null float64\n",
" 21 is_fraud 19302 non-null int64 \n",
"dtypes: float64(5), int64(5), object(12)\n",
"memory usage: 3.2+ MB\n"
]
},
{
"data": {
"text/plain": [
"is_fraud\n",
"0 9651\n",
"1 9651\n",
"Name: count, dtype: int64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.info()\n",
"df.describe()\n",
"df['is_fraud'].value_counts()"
]
},
{
"cell_type": "markdown",
"id": "0ddb32f7",
"metadata": {},
"source": [
"## Feature Engineering"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "13345c23",
"metadata": {},
"outputs": [],
"source": [
"# Convert date fields\n",
"df['trans_date_trans_time'] = pd.to_datetime(df['trans_date_trans_time'])\n",
"df['dob'] = pd.to_datetime(df['dob'])\n",
"\n",
"# Time-based features\n",
"df['hour'] = df['trans_date_trans_time'].dt.hour\n",
"df['day'] = df['trans_date_trans_time'].dt.day\n",
"df['month'] = df['trans_date_trans_time'].dt.month\n",
"df['weekday'] = df['trans_date_trans_time'].dt.weekday\n",
"\n",
"# Age\n",
"df['age'] = (df['trans_date_trans_time'] - df['dob']).dt.days // 365\n",
"\n",
"# Drop high-cardinality or redundant columns\n",
"df = df.drop(columns=['trans_date_trans_time', 'dob', 'trans_num', 'unix_time', 'cc_num', 'street'])\n",
"\n",
"# Encode categorical variables\n",
"cat_cols = ['gender', 'category', 'job', 'merchant', 'first', 'last', 'city', 'state']\n",
"for col in cat_cols:\n",
" df[col] = LabelEncoder().fit_transform(df[col])\n"
]
},
{
"cell_type": "markdown",
"id": "9ee0a1d6",
"metadata": {},
"source": [
"## Feature Selection"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "8c12a37e",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1400x1000 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(14, 10))\n",
"sns.heatmap(df.corr(), cmap='coolwarm', annot=False)\n",
"plt.title(\"Feature Correlation Heatmap\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "91ac8fae",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Features remaining after variance thresholding: 20\n"
]
}
],
"source": [
"X = df.drop('is_fraud', axis=1)\n",
"y = df['is_fraud']\n",
"\n",
"selector = VarianceThreshold(threshold=0.01)\n",
"X_var = selector.fit_transform(X)\n",
"print(f\"Features remaining after variance thresholding: {X_var.shape[1]}\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "8f9f120e",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"forest = RandomForestClassifier(n_estimators=100, random_state=42)\n",
"forest.fit(X, y)\n",
"\n",
"importances = pd.Series(forest.feature_importances_, index=X.columns)\n",
"importances.sort_values().plot(kind='barh', figsize=(10, 8))\n",
"plt.title(\"Random Forest Feature Importances\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "9121633c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Top features selected by RFE:\n",
"['merchant', 'category', 'amt', 'last', 'city', 'city_pop', 'merch_lat', 'merch_long', 'hour', 'age']\n"
]
}
],
"source": [
"rfe = RFE(estimator=RandomForestClassifier(n_estimators=50, random_state=42), n_features_to_select=10)\n",
"rfe.fit(X, y)\n",
"\n",
"selected_features = X.columns[rfe.support_]\n",
"print(\"Top features selected by RFE:\")\n",
"print(selected_features.tolist())"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "784212b7",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>merchant</th>\n",
" <th>category</th>\n",
" <th>amt</th>\n",
" <th>last</th>\n",
" <th>city</th>\n",
" <th>city_pop</th>\n",
" <th>merch_lat</th>\n",
" <th>merch_long</th>\n",
" <th>hour</th>\n",
" <th>age</th>\n",
" <th>is_fraud</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>136</td>\n",
" <td>10</td>\n",
" <td>6.15</td>\n",
" <td>409</td>\n",
" <td>317</td>\n",
" <td>5621</td>\n",
" <td>35.292860</td>\n",
" <td>-81.937193</td>\n",
" <td>18</td>\n",
" <td>47</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>316</td>\n",
" <td>4</td>\n",
" <td>67.35</td>\n",
" <td>144</td>\n",
" <td>62</td>\n",
" <td>18182</td>\n",
" <td>46.228116</td>\n",
" <td>-111.718928</td>\n",
" <td>8</td>\n",
" <td>44</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>383</td>\n",
" <td>8</td>\n",
" <td>1342.69</td>\n",
" <td>407</td>\n",
" <td>546</td>\n",
" <td>1312</td>\n",
" <td>34.898759</td>\n",
" <td>-88.125374</td>\n",
" <td>1</td>\n",
" <td>64</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>422</td>\n",
" <td>10</td>\n",
" <td>28.45</td>\n",
" <td>380</td>\n",
" <td>19</td>\n",
" <td>42384</td>\n",
" <td>41.170642</td>\n",
" <td>-111.052342</td>\n",
" <td>15</td>\n",
" <td>26</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>26</td>\n",
" <td>7</td>\n",
" <td>45.49</td>\n",
" <td>54</td>\n",
" <td>895</td>\n",
" <td>516</td>\n",
" <td>47.489127</td>\n",
" <td>-95.926267</td>\n",
" <td>20</td>\n",
" <td>53</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" merchant category amt last city city_pop merch_lat merch_long \\\n",
"0 136 10 6.15 409 317 5621 35.292860 -81.937193 \n",
"1 316 4 67.35 144 62 18182 46.228116 -111.718928 \n",
"2 383 8 1342.69 407 546 1312 34.898759 -88.125374 \n",
"3 422 10 28.45 380 19 42384 41.170642 -111.052342 \n",
"4 26 7 45.49 54 895 516 47.489127 -95.926267 \n",
"\n",
" hour age is_fraud \n",
"0 18 47 0 \n",
"1 8 44 0 \n",
"2 1 64 0 \n",
"3 15 26 0 \n",
"4 20 53 0 "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_selected = df[selected_features.tolist() + ['is_fraud']]\n",
"df_selected.head()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "a91c3352",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"is_fraud\n",
"0 9651\n",
"1 9651\n",
"Name: count, dtype: int64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['is_fraud'].value_counts()\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "959821de",
"metadata": {},
"outputs": [],
"source": [
"df.to_csv(\"../data/processed/processed_data.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e8269571",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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