From 0bb6828f54b9d9cd3dd4370edf924b88bc58abae Mon Sep 17 00:00:00 2001
From: Pedro Santos <pedrosnts95@gmail.com>
Date: Mon, 6 Mar 2023 13:27:53 +0000
Subject: [PATCH] lab imbalance

---
 your-code/lab_imbalance.ipynb | 629 +++++++++++++++++++++++++++++++++-
 1 file changed, 615 insertions(+), 14 deletions(-)

diff --git a/your-code/lab_imbalance.ipynb b/your-code/lab_imbalance.ipynb
index a3a5359..be92578 100644
--- a/your-code/lab_imbalance.ipynb
+++ b/your-code/lab_imbalance.ipynb
@@ -28,11 +28,164 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here"
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here\n",
+    "finantial = pd.read_csv(\"PS_20174392719_1491204439457_log.csv\")\n",
+    "finantial_samp = finantial.sample(100000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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>step</th>\n",
+       "      <th>type</th>\n",
+       "      <th>amount</th>\n",
+       "      <th>nameOrig</th>\n",
+       "      <th>oldbalanceOrg</th>\n",
+       "      <th>newbalanceOrig</th>\n",
+       "      <th>nameDest</th>\n",
+       "      <th>oldbalanceDest</th>\n",
+       "      <th>newbalanceDest</th>\n",
+       "      <th>isFraud</th>\n",
+       "      <th>isFlaggedFraud</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1635083</th>\n",
+       "      <td>157</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>4742.58</td>\n",
+       "      <td>C3673093</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>M1894780459</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>919804</th>\n",
+       "      <td>43</td>\n",
+       "      <td>CASH_OUT</td>\n",
+       "      <td>6140.55</td>\n",
+       "      <td>C749154626</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>C161392898</td>\n",
+       "      <td>3985947.38</td>\n",
+       "      <td>3992087.93</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4319666</th>\n",
+       "      <td>308</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>9497.51</td>\n",
+       "      <td>C1675562496</td>\n",
+       "      <td>58681.0</td>\n",
+       "      <td>49183.49</td>\n",
+       "      <td>M1464366977</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5797497</th>\n",
+       "      <td>401</td>\n",
+       "      <td>CASH_IN</td>\n",
+       "      <td>96456.19</td>\n",
+       "      <td>C461508864</td>\n",
+       "      <td>1199763.4</td>\n",
+       "      <td>1296219.59</td>\n",
+       "      <td>C2116281134</td>\n",
+       "      <td>101381.88</td>\n",
+       "      <td>4925.69</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3566187</th>\n",
+       "      <td>260</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>4601.19</td>\n",
+       "      <td>C212271533</td>\n",
+       "      <td>631.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>M1667099248</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         step      type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
+       "1635083   157   PAYMENT   4742.58     C3673093            0.0            0.00   \n",
+       "919804     43  CASH_OUT   6140.55   C749154626            0.0            0.00   \n",
+       "4319666   308   PAYMENT   9497.51  C1675562496        58681.0        49183.49   \n",
+       "5797497   401   CASH_IN  96456.19   C461508864      1199763.4      1296219.59   \n",
+       "3566187   260   PAYMENT   4601.19   C212271533          631.0            0.00   \n",
+       "\n",
+       "            nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
+       "1635083  M1894780459            0.00            0.00        0               0  \n",
+       "919804    C161392898      3985947.38      3992087.93        0               0  \n",
+       "4319666  M1464366977            0.00            0.00        0               0  \n",
+       "5797497  C2116281134       101381.88         4925.69        0               0  \n",
+       "3566187  M1667099248            0.00            0.00        0               0  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "finantial_samp.head()"
    ]
   },
   {
@@ -44,11 +197,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    99872\n",
+      "1      128\n",
+      "Name: isFraud, dtype: int64\n",
+      "0    100000\n",
+      "Name: isFlaggedFraud, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your response here"
+    "# Your response here\n",
+    "finantial_samp[\"isFraud\"].value_counts().plot(kind=\"bar\")\n",
+    "print(finantial_samp[\"isFraud\"].value_counts())\n",
+    "print(finantial_samp[\"isFlaggedFraud\"].value_counts())"
    ]
   },
   {
@@ -60,11 +238,136 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "step              0\n",
+       "type              0\n",
+       "amount            0\n",
+       "nameOrig          0\n",
+       "oldbalanceOrg     0\n",
+       "newbalanceOrig    0\n",
+       "nameDest          0\n",
+       "oldbalanceDest    0\n",
+       "newbalanceDest    0\n",
+       "isFraud           0\n",
+       "isFlaggedFraud    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "finantial_samp.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "finantial_samp.dtypes\n",
+    "finantial_samp[\"type\"].value_counts()\n",
+    "typecol = pd.get_dummies(finantial_samp[\"type\"],prefix = \"type\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here\n"
+    "finantial_samp = pd.concat([finantial_samp, typecol], axis=1, join='outer')\n",
+    "finantial_samp.drop(columns = \"type\", axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "finantial_samp.drop(columns = [\"nameDest\",\"nameOrig\"], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "step                int64\n",
+       "amount            float64\n",
+       "oldbalanceOrg     float64\n",
+       "newbalanceOrig    float64\n",
+       "oldbalanceDest    float64\n",
+       "newbalanceDest    float64\n",
+       "isFraud             int64\n",
+       "isFlaggedFraud      int64\n",
+       "type_CASH_IN        uint8\n",
+       "type_CASH_OUT       uint8\n",
+       "type_DEBIT          uint8\n",
+       "type_PAYMENT        uint8\n",
+       "type_TRANSFER       uint8\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "finantial_samp.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\pedro\\AppData\\Local\\Temp\\ipykernel_29080\\2957104802.py:3: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  mask = np.zeros_like(corr, dtype=np.bool)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1400x1400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "corr=np.abs(finantial_samp.corr())\n",
+    "#Set up mask for triangle representation\n",
+    "mask = np.zeros_like(corr, dtype=np.bool)\n",
+    "mask[np.triu_indices_from(mask)] = True\n",
+    "# Set up the matplotlib figure\n",
+    "f, ax = plt.subplots(figsize=(14, 14))\n",
+    "# Generate a custom diverging colormap\n",
+    "cmap = sn.diverging_palette(220, 10, as_cmap=True)\n",
+    "# Draw the heatmap with the mask and correct aspect ratio\n",
+    "sn.heatmap(corr, mask=mask,  vmax=1,square=True, linewidths=.5, cbar_kws={\"shrink\": .5},annot = corr)\n",
+    "plt.show()"
    ]
   },
   {
@@ -76,11 +379,94 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here"
+    "# Your code here\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import classification_report, confusion_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(finantial_samp.drop(columns = \"isFraud\",axis=1), finantial_samp[\"isFraud\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test data accuracy is 0.99844\n",
+      "train data accuracy is 0.9985066666666667\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = LogisticRegression(max_iter=10000)\n",
+    "\n",
+    "model.fit(x_train,y_train)\n",
+    "\n",
+    "# score\n",
+    "print(\"test data accuracy is\", model.score(x_test, y_test))\n",
+    "print(\"train data accuracy is\", model.score(x_train, y_train))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00     24972\n",
+      "           1       0.38      0.64      0.48        28\n",
+      "\n",
+      "    accuracy                           1.00     25000\n",
+      "   macro avg       0.69      0.82      0.74     25000\n",
+      "weighted avg       1.00      1.00      1.00     25000\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "pred = model.predict(x_test)\n",
+    "print(classification_report(y_test,pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[24943,    29],\n",
+       "       [   10,    18]], dtype=int64)"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "confusion_matrix(y_test, pred)"
    ]
   },
   {
@@ -92,11 +478,220 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lets try oversampling\n",
+    "\n",
+    "train = pd.concat([x_train,y_train],axis=1)\n",
+    "\n",
+    "no_fraud = train[train[\"isFraud\"]==0]\n",
+    "yes_fraud = train[train[\"isFraud\"]==1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(74900, 13)\n",
+      "(100, 13)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(no_fraud.shape)\n",
+    "print(yes_fraud.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.utils import resample \n",
+    "yes_fraud_oversampled = resample(yes_fraud, replace=True,n_samples = len(no_fraud))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_over = pd.concat([no_fraud, yes_fraud_oversampled])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here"
+    "x_train_over = train_over.drop(\"isFraud\", axis=1)\n",
+    "y_train_over = train_over[\"isFraud\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test data accuracy is 0.98696\n",
+      "train data accuracy is 0.9935781041388518\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.fit(x_train_over,y_train_over)\n",
+    "\n",
+    "# score\n",
+    "print(\"test data accuracy is\", model.score(x_test, y_test))\n",
+    "print(\"train data accuracy is\", model.score(x_train_over, y_train_over))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      0.99      0.99     24972\n",
+      "           1       0.08      1.00      0.15        28\n",
+      "\n",
+      "    accuracy                           0.99     25000\n",
+      "   macro avg       0.54      0.99      0.57     25000\n",
+      "weighted avg       1.00      0.99      0.99     25000\n",
+      "\n",
+      "[[24646   326]\n",
+      " [    0    28]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "pred = model.predict(x_test)\n",
+    "print(classification_report(y_test,pred))\n",
+    "print(confusion_matrix(y_test, pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test data accuracy is 0.99896\n",
+      "train data accuracy is 0.9989733333333334\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "\n",
+    "tree = DecisionTreeClassifier(max_depth=3)\n",
+    "\n",
+    "tree.fit(x_train,y_train)\n",
+    "print(\"test data accuracy is\", tree.score(x_test, y_test))\n",
+    "print(\"train data accuracy is\", tree.score(x_train, y_train))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00     24972\n",
+      "           1       1.00      0.07      0.13        28\n",
+      "\n",
+      "    accuracy                           1.00     25000\n",
+      "   macro avg       1.00      0.54      0.57     25000\n",
+      "weighted avg       1.00      1.00      1.00     25000\n",
+      "\n",
+      "[[24972     0]\n",
+      " [   26     2]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "pred = tree.predict(x_test)\n",
+    "print(classification_report(y_test,pred))\n",
+    "print(confusion_matrix(y_test, pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test data accuracy is 0.97248\n",
+      "train data accuracy is 0.9601268357810414\n"
+     ]
+    }
+   ],
+   "source": [
+    "tree = DecisionTreeClassifier(max_depth=3)\n",
+    "\n",
+    "tree.fit(x_train_over,y_train_over)\n",
+    "print(\"test data accuracy is\", tree.score(x_test, y_test))\n",
+    "print(\"train data accuracy is\", tree.score(x_train_over, y_train_over))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      0.97      0.99     24972\n",
+      "           1       0.04      0.96      0.07        28\n",
+      "\n",
+      "    accuracy                           0.97     25000\n",
+      "   macro avg       0.52      0.97      0.53     25000\n",
+      "weighted avg       1.00      0.97      0.99     25000\n",
+      "\n",
+      "[[24285   687]\n",
+      " [    1    27]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "pred = tree.predict(x_test)\n",
+    "print(classification_report(y_test,pred))\n",
+    "print(confusion_matrix(y_test, pred))"
    ]
   },
   {
@@ -112,7 +707,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your response here"
+    "# Your response here\n",
+    "\"\"\"\n",
+    "I believe the logistic regression model (after oversampling) worked better since the it managed to have zero false negatives\n",
+    "this means we managed to catch all fraudulent transactions\n",
+    "even though we had more cases reported as fraud which turned out to not be, in this case\n",
+    "its better to flag and realize its not true then to not flag and let a fraudulent transactionpass through\n",
+    "\"\"\""
    ]
   },
   {
@@ -125,7 +726,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -139,7 +740,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.9.13"
   }
  },
  "nbformat": 4,