diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..03b3a01
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+*.csv
+*.zip
diff --git a/your-code/lab_imbalance.ipynb b/your-code/lab_imbalance.ipynb
index dbb15e1..0e21837 100644
--- a/your-code/lab_imbalance.ipynb
+++ b/your-code/lab_imbalance.ipynb
@@ -1,147 +1,2599 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Inbalanced Classes\n",
-    "## In this lab, we are going to explore a case of imbalanced classes. \n",
-    "\n",
-    "\n",
-    "Like we disussed in class, when we have noisy data, if we are not careful, we can end up fitting our model to the noise in the data and not the 'signal'-- the factors that actually determine the outcome. This is called overfitting, and results in good results in training, and in bad results when the model is applied to real data. Similarly, we could have a model that is too simplistic to accurately model the signal. This produces a model that doesnt work well (ever). \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Note: before doing the first commit, make sure you don't include the large csv file, either by adding it to .gitignore, or by deleting it."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### First, download the data from: https://www.kaggle.com/datasets/chitwanmanchanda/fraudulent-transactions-data?resource=download . Import the dataset and provide some discriptive statistics and plots. What do you think will be the important features in determining the outcome?\n",
-    "### Note: don't use the entire dataset, use a sample instead, with n=100000 elements, so your computer doesn't freeze."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### What is the distribution of the outcome? "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your response here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Clean the dataset. Pre-process it to make it suitable for ML training. Feel free to explore, drop, encode, transform, etc. Whatever you feel will improve the model score."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Run a logisitc regression classifier and evaluate its accuracy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Now pick a model of your choice and evaluate its accuracy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Which model worked better and how do you know?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your response here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Note: before doing the first commit, make sure you don't include the large csv file, either by adding it to .gitignore, or by deleting it."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.6.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Inbalanced Classes\n",
+    "## In this lab, we are going to explore a case of imbalanced classes. \n",
+    "\n",
+    "\n",
+    "Like we disussed in class, when we have noisy data, if we are not careful, we can end up fitting our model to the noise in the data and not the 'signal'-- the factors that actually determine the outcome. This is called overfitting, and results in good results in training, and in bad results when the model is applied to real data. Similarly, we could have a model that is too simplistic to accurately model the signal. This produces a model that doesnt work well (ever). \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Note: before doing the first commit, make sure you don't include the large csv file, either by adding it to .gitignore, or by deleting it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### First, download the data from: https://www.kaggle.com/datasets/chitwanmanchanda/fraudulent-transactions-data?resource=download . Import the dataset and provide some discriptive statistics and plots. What do you think will be the important features in determining the outcome?\n",
+    "### Note: don't use the entire dataset, use a sample instead, with n=100000 elements, so your computer doesn't freeze."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What is the distribution of the outcome? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "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>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>9839.64</td>\n",
+       "      <td>C1231006815</td>\n",
+       "      <td>170136.0</td>\n",
+       "      <td>160296.36</td>\n",
+       "      <td>M1979787155</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>1864.28</td>\n",
+       "      <td>C1666544295</td>\n",
+       "      <td>21249.0</td>\n",
+       "      <td>19384.72</td>\n",
+       "      <td>M2044282225</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>TRANSFER</td>\n",
+       "      <td>181.00</td>\n",
+       "      <td>C1305486145</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>C553264065</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>CASH_OUT</td>\n",
+       "      <td>181.00</td>\n",
+       "      <td>C840083671</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>C38997010</td>\n",
+       "      <td>21182.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>11668.14</td>\n",
+       "      <td>C2048537720</td>\n",
+       "      <td>41554.0</td>\n",
+       "      <td>29885.86</td>\n",
+       "      <td>M1230701703</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</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",
+       "0     1   PAYMENT   9839.64  C1231006815       170136.0       160296.36   \n",
+       "1     1   PAYMENT   1864.28  C1666544295        21249.0        19384.72   \n",
+       "2     1  TRANSFER    181.00  C1305486145          181.0            0.00   \n",
+       "3     1  CASH_OUT    181.00   C840083671          181.0            0.00   \n",
+       "4     1   PAYMENT  11668.14  C2048537720        41554.0        29885.86   \n",
+       "\n",
+       "      nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
+       "0  M1979787155             0.0             0.0        0               0  \n",
+       "1  M2044282225             0.0             0.0        0               0  \n",
+       "2   C553264065             0.0             0.0        1               0  \n",
+       "3    C38997010         21182.0             0.0        1               0  \n",
+       "4  M1230701703             0.0             0.0        0               0  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Your response here\n",
+    "import zipfile\n",
+    "\n",
+    "with zipfile.ZipFile(\"D:\\IronHack\\Study\\Bootcamp\\labs\\week 19\\lab-imbalance\\Fraud.csv.zip\",\"r\") as z:\n",
+    "    z.extractall(\".\")\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "fraud = pd.read_csv(\"Fraud.csv\",nrows=100000)\n",
+    "display(fraud.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Clean the dataset. Pre-process it to make it suitable for ML training. Feel free to explore, drop, encode, transform, etc. Whatever you feel will improve the model score."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 100000 entries, 0 to 99999\n",
+      "Data columns (total 11 columns):\n",
+      " #   Column          Non-Null Count   Dtype  \n",
+      "---  ------          --------------   -----  \n",
+      " 0   step            100000 non-null  int64  \n",
+      " 1   type            100000 non-null  object \n",
+      " 2   amount          100000 non-null  float64\n",
+      " 3   nameOrig        100000 non-null  object \n",
+      " 4   oldbalanceOrg   100000 non-null  float64\n",
+      " 5   newbalanceOrig  100000 non-null  float64\n",
+      " 6   nameDest        100000 non-null  object \n",
+      " 7   oldbalanceDest  100000 non-null  float64\n",
+      " 8   newbalanceDest  100000 non-null  float64\n",
+      " 9   isFraud         100000 non-null  int64  \n",
+      " 10  isFlaggedFraud  100000 non-null  int64  \n",
+      "dtypes: float64(5), int64(3), object(3)\n",
+      "memory usage: 8.4+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "fraud.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>amount</th>\n",
+       "      <th>oldbalanceOrg</th>\n",
+       "      <th>newbalanceOrig</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>count</th>\n",
+       "      <td>100000.000000</td>\n",
+       "      <td>1.000000e+05</td>\n",
+       "      <td>1.000000e+05</td>\n",
+       "      <td>1.000000e+05</td>\n",
+       "      <td>1.000000e+05</td>\n",
+       "      <td>1.000000e+05</td>\n",
+       "      <td>100000.000000</td>\n",
+       "      <td>100000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>8.499640</td>\n",
+       "      <td>1.736022e+05</td>\n",
+       "      <td>8.777575e+05</td>\n",
+       "      <td>8.940619e+05</td>\n",
+       "      <td>8.805048e+05</td>\n",
+       "      <td>1.184041e+06</td>\n",
+       "      <td>0.001160</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.825545</td>\n",
+       "      <td>3.443003e+05</td>\n",
+       "      <td>2.673284e+06</td>\n",
+       "      <td>2.711318e+06</td>\n",
+       "      <td>2.402267e+06</td>\n",
+       "      <td>2.802350e+06</td>\n",
+       "      <td>0.034039</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.200000e-01</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>9.963562e+03</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>5.274552e+04</td>\n",
+       "      <td>2.006150e+04</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>2.083943e+04</td>\n",
+       "      <td>4.990918e+04</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>2.117631e+05</td>\n",
+       "      <td>1.901920e+05</td>\n",
+       "      <td>2.148132e+05</td>\n",
+       "      <td>5.882724e+05</td>\n",
+       "      <td>1.058186e+06</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>1.000000e+07</td>\n",
+       "      <td>3.379739e+07</td>\n",
+       "      <td>3.400874e+07</td>\n",
+       "      <td>3.400874e+07</td>\n",
+       "      <td>3.894623e+07</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                step        amount  oldbalanceOrg  newbalanceOrig  \\\n",
+       "count  100000.000000  1.000000e+05   1.000000e+05    1.000000e+05   \n",
+       "mean        8.499640  1.736022e+05   8.777575e+05    8.940619e+05   \n",
+       "std         1.825545  3.443003e+05   2.673284e+06    2.711318e+06   \n",
+       "min         1.000000  3.200000e-01   0.000000e+00    0.000000e+00   \n",
+       "25%         8.000000  9.963562e+03   0.000000e+00    0.000000e+00   \n",
+       "50%         9.000000  5.274552e+04   2.006150e+04    0.000000e+00   \n",
+       "75%        10.000000  2.117631e+05   1.901920e+05    2.148132e+05   \n",
+       "max        10.000000  1.000000e+07   3.379739e+07    3.400874e+07   \n",
+       "\n",
+       "       oldbalanceDest  newbalanceDest        isFraud  isFlaggedFraud  \n",
+       "count    1.000000e+05    1.000000e+05  100000.000000        100000.0  \n",
+       "mean     8.805048e+05    1.184041e+06       0.001160             0.0  \n",
+       "std      2.402267e+06    2.802350e+06       0.034039             0.0  \n",
+       "min      0.000000e+00    0.000000e+00       0.000000             0.0  \n",
+       "25%      0.000000e+00    0.000000e+00       0.000000             0.0  \n",
+       "50%      2.083943e+04    4.990918e+04       0.000000             0.0  \n",
+       "75%      5.882724e+05    1.058186e+06       0.000000             0.0  \n",
+       "max      3.400874e+07    3.894623e+07       1.000000             0.0  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fraud.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "isFraud\n",
+       "0    99884\n",
+       "1      116\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fraud['isFraud'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Target is very imbalanced"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dummies = pd.get_dummies(fraud['type'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>CASH_IN</th>\n",
+       "      <th>CASH_OUT</th>\n",
+       "      <th>DEBIT</th>\n",
+       "      <th>PAYMENT</th>\n",
+       "      <th>TRANSFER</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   CASH_IN  CASH_OUT  DEBIT  PAYMENT  TRANSFER\n",
+       "0    False     False  False     True     False\n",
+       "1    False     False  False     True     False\n",
+       "2    False     False  False    False      True\n",
+       "3    False      True  False    False     False\n",
+       "4    False     False  False     True     False"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dummies.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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",
+       "      <th>CASH_IN</th>\n",
+       "      <th>CASH_OUT</th>\n",
+       "      <th>DEBIT</th>\n",
+       "      <th>PAYMENT</th>\n",
+       "      <th>TRANSFER</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>9839.64</td>\n",
+       "      <td>C1231006815</td>\n",
+       "      <td>170136.0</td>\n",
+       "      <td>160296.36</td>\n",
+       "      <td>M1979787155</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>1864.28</td>\n",
+       "      <td>C1666544295</td>\n",
+       "      <td>21249.0</td>\n",
+       "      <td>19384.72</td>\n",
+       "      <td>M2044282225</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>TRANSFER</td>\n",
+       "      <td>181.00</td>\n",
+       "      <td>C1305486145</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>C553264065</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>CASH_OUT</td>\n",
+       "      <td>181.00</td>\n",
+       "      <td>C840083671</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>C38997010</td>\n",
+       "      <td>21182.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>11668.14</td>\n",
+       "      <td>C2048537720</td>\n",
+       "      <td>41554.0</td>\n",
+       "      <td>29885.86</td>\n",
+       "      <td>M1230701703</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99995</th>\n",
+       "      <td>10</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>4020.66</td>\n",
+       "      <td>C1410794718</td>\n",
+       "      <td>159929.0</td>\n",
+       "      <td>155908.34</td>\n",
+       "      <td>M1257036576</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99996</th>\n",
+       "      <td>10</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>18345.49</td>\n",
+       "      <td>C744303677</td>\n",
+       "      <td>6206.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>M1785344556</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99997</th>\n",
+       "      <td>10</td>\n",
+       "      <td>CASH_IN</td>\n",
+       "      <td>183774.91</td>\n",
+       "      <td>C104331851</td>\n",
+       "      <td>39173.0</td>\n",
+       "      <td>222947.91</td>\n",
+       "      <td>C36392889</td>\n",
+       "      <td>54925.05</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99998</th>\n",
+       "      <td>10</td>\n",
+       "      <td>CASH_OUT</td>\n",
+       "      <td>82237.17</td>\n",
+       "      <td>C707662966</td>\n",
+       "      <td>6031.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>C1553004158</td>\n",
+       "      <td>592635.66</td>\n",
+       "      <td>799140.46</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99999</th>\n",
+       "      <td>10</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>20096.56</td>\n",
+       "      <td>C1868032458</td>\n",
+       "      <td>110117.0</td>\n",
+       "      <td>90020.44</td>\n",
+       "      <td>M1419201886</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100000 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       step      type     amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
+       "0         1   PAYMENT    9839.64  C1231006815       170136.0       160296.36   \n",
+       "1         1   PAYMENT    1864.28  C1666544295        21249.0        19384.72   \n",
+       "2         1  TRANSFER     181.00  C1305486145          181.0            0.00   \n",
+       "3         1  CASH_OUT     181.00   C840083671          181.0            0.00   \n",
+       "4         1   PAYMENT   11668.14  C2048537720        41554.0        29885.86   \n",
+       "...     ...       ...        ...          ...            ...             ...   \n",
+       "99995    10   PAYMENT    4020.66  C1410794718       159929.0       155908.34   \n",
+       "99996    10   PAYMENT   18345.49   C744303677         6206.0            0.00   \n",
+       "99997    10   CASH_IN  183774.91   C104331851        39173.0       222947.91   \n",
+       "99998    10  CASH_OUT   82237.17   C707662966         6031.0            0.00   \n",
+       "99999    10   PAYMENT   20096.56  C1868032458       110117.0        90020.44   \n",
+       "\n",
+       "          nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \\\n",
+       "0      M1979787155            0.00            0.00        0               0   \n",
+       "1      M2044282225            0.00            0.00        0               0   \n",
+       "2       C553264065            0.00            0.00        1               0   \n",
+       "3        C38997010        21182.00            0.00        1               0   \n",
+       "4      M1230701703            0.00            0.00        0               0   \n",
+       "...            ...             ...             ...      ...             ...   \n",
+       "99995  M1257036576            0.00            0.00        0               0   \n",
+       "99996  M1785344556            0.00            0.00        0               0   \n",
+       "99997    C36392889        54925.05            0.00        0               0   \n",
+       "99998  C1553004158       592635.66       799140.46        0               0   \n",
+       "99999  M1419201886            0.00            0.00        0               0   \n",
+       "\n",
+       "       CASH_IN  CASH_OUT  DEBIT  PAYMENT  TRANSFER  \n",
+       "0        False     False  False     True     False  \n",
+       "1        False     False  False     True     False  \n",
+       "2        False     False  False    False      True  \n",
+       "3        False      True  False    False     False  \n",
+       "4        False     False  False     True     False  \n",
+       "...        ...       ...    ...      ...       ...  \n",
+       "99995    False     False  False     True     False  \n",
+       "99996    False     False  False     True     False  \n",
+       "99997     True     False  False    False     False  \n",
+       "99998    False      True  False    False     False  \n",
+       "99999    False     False  False     True     False  \n",
+       "\n",
+       "[100000 rows x 16 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fraud_with_dummies = pd.concat([fraud, dummies], axis=1)\n",
+    "\n",
+    "display(fraud_with_dummies)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nameOrig\n",
+       "C1231006815    1\n",
+       "C1407775146    1\n",
+       "C1802124274    1\n",
+       "C1910868687    1\n",
+       "C579725406     1\n",
+       "              ..\n",
+       "C668560116     1\n",
+       "C986934168     1\n",
+       "C1567015632    1\n",
+       "C1280981431    1\n",
+       "C1868032458    1\n",
+       "Name: count, Length: 100000, dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fraud_with_dummies['nameOrig'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nameDest\n",
+       "C985934102     78\n",
+       "C1286084959    72\n",
+       "C248609774     71\n",
+       "C1590550415    69\n",
+       "C2083562754    66\n",
+       "               ..\n",
+       "M259075709      1\n",
+       "M375566378      1\n",
+       "M1225444842     1\n",
+       "M1709995863     1\n",
+       "M1419201886     1\n",
+       "Name: count, Length: 51551, dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fraud_with_dummies['nameDest'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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",
+       "      <th>CASH_IN</th>\n",
+       "      <th>CASH_OUT</th>\n",
+       "      <th>DEBIT</th>\n",
+       "      <th>PAYMENT</th>\n",
+       "      <th>TRANSFER</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>9839.64</td>\n",
+       "      <td>11862</td>\n",
+       "      <td>170136.0</td>\n",
+       "      <td>160296.36</td>\n",
+       "      <td>31981</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>1864.28</td>\n",
+       "      <td>34345</td>\n",
+       "      <td>21249.0</td>\n",
+       "      <td>19384.72</td>\n",
+       "      <td>33288</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>TRANSFER</td>\n",
+       "      <td>181.00</td>\n",
+       "      <td>15636</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>9303</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>CASH_OUT</td>\n",
+       "      <td>181.00</td>\n",
+       "      <td>91777</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>8273</td>\n",
+       "      <td>21182.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>11668.14</td>\n",
+       "      <td>54041</td>\n",
+       "      <td>41554.0</td>\n",
+       "      <td>29885.86</td>\n",
+       "      <td>16681</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99995</th>\n",
+       "      <td>10</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>4020.66</td>\n",
+       "      <td>21051</td>\n",
+       "      <td>159929.0</td>\n",
+       "      <td>155908.34</td>\n",
+       "      <td>17200</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99996</th>\n",
+       "      <td>10</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>18345.49</td>\n",
+       "      <td>86688</td>\n",
+       "      <td>6206.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>28011</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99997</th>\n",
+       "      <td>10</td>\n",
+       "      <td>CASH_IN</td>\n",
+       "      <td>183774.91</td>\n",
+       "      <td>2198</td>\n",
+       "      <td>39173.0</td>\n",
+       "      <td>222947.91</td>\n",
+       "      <td>8118</td>\n",
+       "      <td>54925.05</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99998</th>\n",
+       "      <td>10</td>\n",
+       "      <td>CASH_OUT</td>\n",
+       "      <td>82237.17</td>\n",
+       "      <td>84758</td>\n",
+       "      <td>6031.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3451</td>\n",
+       "      <td>592635.66</td>\n",
+       "      <td>799140.46</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99999</th>\n",
+       "      <td>10</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>20096.56</td>\n",
+       "      <td>44792</td>\n",
+       "      <td>110117.0</td>\n",
+       "      <td>90020.44</td>\n",
+       "      <td>20468</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100000 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       step      type     amount  nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
+       "0         1   PAYMENT    9839.64     11862       170136.0       160296.36   \n",
+       "1         1   PAYMENT    1864.28     34345        21249.0        19384.72   \n",
+       "2         1  TRANSFER     181.00     15636          181.0            0.00   \n",
+       "3         1  CASH_OUT     181.00     91777          181.0            0.00   \n",
+       "4         1   PAYMENT   11668.14     54041        41554.0        29885.86   \n",
+       "...     ...       ...        ...       ...            ...             ...   \n",
+       "99995    10   PAYMENT    4020.66     21051       159929.0       155908.34   \n",
+       "99996    10   PAYMENT   18345.49     86688         6206.0            0.00   \n",
+       "99997    10   CASH_IN  183774.91      2198        39173.0       222947.91   \n",
+       "99998    10  CASH_OUT   82237.17     84758         6031.0            0.00   \n",
+       "99999    10   PAYMENT   20096.56     44792       110117.0        90020.44   \n",
+       "\n",
+       "       nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \\\n",
+       "0         31981            0.00            0.00        0               0   \n",
+       "1         33288            0.00            0.00        0               0   \n",
+       "2          9303            0.00            0.00        1               0   \n",
+       "3          8273        21182.00            0.00        1               0   \n",
+       "4         16681            0.00            0.00        0               0   \n",
+       "...         ...             ...             ...      ...             ...   \n",
+       "99995     17200            0.00            0.00        0               0   \n",
+       "99996     28011            0.00            0.00        0               0   \n",
+       "99997      8118        54925.05            0.00        0               0   \n",
+       "99998      3451       592635.66       799140.46        0               0   \n",
+       "99999     20468            0.00            0.00        0               0   \n",
+       "\n",
+       "       CASH_IN  CASH_OUT  DEBIT  PAYMENT  TRANSFER  \n",
+       "0        False     False  False     True     False  \n",
+       "1        False     False  False     True     False  \n",
+       "2        False     False  False    False      True  \n",
+       "3        False      True  False    False     False  \n",
+       "4        False     False  False     True     False  \n",
+       "...        ...       ...    ...      ...       ...  \n",
+       "99995    False     False  False     True     False  \n",
+       "99996    False     False  False     True     False  \n",
+       "99997     True     False  False    False     False  \n",
+       "99998    False      True  False    False     False  \n",
+       "99999    False     False  False     True     False  \n",
+       "\n",
+       "[100000 rows x 16 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "le=LabelEncoder()\n",
+    "fraud_with_dummies['nameOrig']=le.fit_transform(fraud_with_dummies['nameOrig'])\n",
+    "fraud_with_dummies['nameDest']=le.fit_transform(fraud_with_dummies['nameDest'])\n",
+    "\n",
+    "display(fraud_with_dummies)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "numeric_columns = fraud_with_dummies.select_dtypes(include=['int64', 'float64'])\n",
+    "\n",
+    "correlation_matrix = numeric_columns.corr()\n",
+    "\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.heatmap(correlation_matrix, annot=True, cmap='YlOrRd')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fraud_with_dummies.drop(['oldbalanceOrg','oldbalanceDest','type','isFlaggedFraud'] ,axis =1, inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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>amount</th>\n",
+       "      <th>nameOrig</th>\n",
+       "      <th>newbalanceOrig</th>\n",
+       "      <th>nameDest</th>\n",
+       "      <th>newbalanceDest</th>\n",
+       "      <th>isFraud</th>\n",
+       "      <th>CASH_IN</th>\n",
+       "      <th>CASH_OUT</th>\n",
+       "      <th>DEBIT</th>\n",
+       "      <th>PAYMENT</th>\n",
+       "      <th>TRANSFER</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>9839.64</td>\n",
+       "      <td>11862</td>\n",
+       "      <td>160296.36</td>\n",
+       "      <td>31981</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1864.28</td>\n",
+       "      <td>34345</td>\n",
+       "      <td>19384.72</td>\n",
+       "      <td>33288</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>181.00</td>\n",
+       "      <td>15636</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>9303</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>181.00</td>\n",
+       "      <td>91777</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>8273</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>11668.14</td>\n",
+       "      <td>54041</td>\n",
+       "      <td>29885.86</td>\n",
+       "      <td>16681</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99995</th>\n",
+       "      <td>10</td>\n",
+       "      <td>4020.66</td>\n",
+       "      <td>21051</td>\n",
+       "      <td>155908.34</td>\n",
+       "      <td>17200</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99996</th>\n",
+       "      <td>10</td>\n",
+       "      <td>18345.49</td>\n",
+       "      <td>86688</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>28011</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99997</th>\n",
+       "      <td>10</td>\n",
+       "      <td>183774.91</td>\n",
+       "      <td>2198</td>\n",
+       "      <td>222947.91</td>\n",
+       "      <td>8118</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99998</th>\n",
+       "      <td>10</td>\n",
+       "      <td>82237.17</td>\n",
+       "      <td>84758</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3451</td>\n",
+       "      <td>799140.46</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99999</th>\n",
+       "      <td>10</td>\n",
+       "      <td>20096.56</td>\n",
+       "      <td>44792</td>\n",
+       "      <td>90020.44</td>\n",
+       "      <td>20468</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100000 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       step     amount  nameOrig  newbalanceOrig  nameDest  newbalanceDest  \\\n",
+       "0         1    9839.64     11862       160296.36     31981            0.00   \n",
+       "1         1    1864.28     34345        19384.72     33288            0.00   \n",
+       "2         1     181.00     15636            0.00      9303            0.00   \n",
+       "3         1     181.00     91777            0.00      8273            0.00   \n",
+       "4         1   11668.14     54041        29885.86     16681            0.00   \n",
+       "...     ...        ...       ...             ...       ...             ...   \n",
+       "99995    10    4020.66     21051       155908.34     17200            0.00   \n",
+       "99996    10   18345.49     86688            0.00     28011            0.00   \n",
+       "99997    10  183774.91      2198       222947.91      8118            0.00   \n",
+       "99998    10   82237.17     84758            0.00      3451       799140.46   \n",
+       "99999    10   20096.56     44792        90020.44     20468            0.00   \n",
+       "\n",
+       "       isFraud  CASH_IN  CASH_OUT  DEBIT  PAYMENT  TRANSFER  \n",
+       "0            0    False     False  False     True     False  \n",
+       "1            0    False     False  False     True     False  \n",
+       "2            1    False     False  False    False      True  \n",
+       "3            1    False      True  False    False     False  \n",
+       "4            0    False     False  False     True     False  \n",
+       "...        ...      ...       ...    ...      ...       ...  \n",
+       "99995        0    False     False  False     True     False  \n",
+       "99996        0    False     False  False     True     False  \n",
+       "99997        0     True     False  False    False     False  \n",
+       "99998        0    False      True  False    False     False  \n",
+       "99999        0    False     False  False     True     False  \n",
+       "\n",
+       "[100000 rows x 12 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(fraud_with_dummies)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = fraud_with_dummies.drop('isFraud', axis=1) \n",
+    "y = fraud_with_dummies['isFraud']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run a logisitc regression classifier and evaluate its accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = LogisticRegression()\n",
+    "model = model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9989"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "pred = model.predict(X_test)\n",
+    "accuracy_score(y_test,pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[19978,     0],\n",
+       "       [   22,     0]], dtype=int64)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "confusion_matrix(y_test,pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00     19978\n",
+      "           1       0.00      0.00      0.00        22\n",
+      "\n",
+      "    accuracy                           1.00     20000\n",
+      "   macro avg       0.50      0.50      0.50     20000\n",
+      "weighted avg       1.00      1.00      1.00     20000\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Yuliya Lavrenyuk\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+      "c:\\Users\\Yuliya Lavrenyuk\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
+      "c:\\Users\\Yuliya Lavrenyuk\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "print(classification_report(y_test,pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The model has a perfect accuracy of 1.00, but this is misleading because the dataset is highly imbalanced. The model is simply predicting the majority class (class 0) almost all the time.\n",
+    "Failure to Identify Fraud (class 1): The model fails to correctly identify any instance of class 1 (fraud). This is evident from the 0 precision, recall, and F1-score for class 1.\n",
+    "Imbalance Issue: This suggests that the model is heavily biased towards the majority class (class 0) and is not effective at detecting fraud (class 1). In real-world applications like fraud detection, such a model would be inadequate because it would miss all fraudulent cases."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now pick a model of your choice and evaluate its accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here\n",
+    "#Let's try to do oversampling :\n",
+    "\n",
+    "fraud = fraud_with_dummies[fraud_with_dummies['isFraud'] ==1]\n",
+    "no_fraud = fraud_with_dummies[fraud_with_dummies['isFraud'] == 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(116, 12)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(99884, 12)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(fraud.shape)\n",
+    "display(no_fraud.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.utils import resample"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# oversample minority\n",
+    "yes_fraud_oversampled = resample(fraud, #<- sample from here\n",
+    "                                    replace=True, #<- we need replacement, since we don't have enough data otherwise\n",
+    "                                    n_samples = len(no_fraud),#<- make both sets the same size\n",
+    "                                    random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "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>amount</th>\n",
+       "      <th>nameOrig</th>\n",
+       "      <th>newbalanceOrig</th>\n",
+       "      <th>nameDest</th>\n",
+       "      <th>newbalanceDest</th>\n",
+       "      <th>isFraud</th>\n",
+       "      <th>CASH_IN</th>\n",
+       "      <th>CASH_OUT</th>\n",
+       "      <th>DEBIT</th>\n",
+       "      <th>PAYMENT</th>\n",
+       "      <th>TRANSFER</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>9839.64</td>\n",
+       "      <td>11862</td>\n",
+       "      <td>160296.36</td>\n",
+       "      <td>31981</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1864.28</td>\n",
+       "      <td>34345</td>\n",
+       "      <td>19384.72</td>\n",
+       "      <td>33288</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>11668.14</td>\n",
+       "      <td>54041</td>\n",
+       "      <td>29885.86</td>\n",
+       "      <td>16681</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1</td>\n",
+       "      <td>7817.71</td>\n",
+       "      <td>94819</td>\n",
+       "      <td>46042.29</td>\n",
+       "      <td>42727</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1</td>\n",
+       "      <td>7107.77</td>\n",
+       "      <td>28265</td>\n",
+       "      <td>176087.23</td>\n",
+       "      <td>39343</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   step    amount  nameOrig  newbalanceOrig  nameDest  newbalanceDest  \\\n",
+       "0     1   9839.64     11862       160296.36     31981             0.0   \n",
+       "1     1   1864.28     34345        19384.72     33288             0.0   \n",
+       "4     1  11668.14     54041        29885.86     16681             0.0   \n",
+       "5     1   7817.71     94819        46042.29     42727             0.0   \n",
+       "6     1   7107.77     28265       176087.23     39343             0.0   \n",
+       "\n",
+       "   isFraud  CASH_IN  CASH_OUT  DEBIT  PAYMENT  TRANSFER  \n",
+       "0        0    False     False  False     True     False  \n",
+       "1        0    False     False  False     True     False  \n",
+       "4        0    False     False  False     True     False  \n",
+       "5        0    False     False  False     True     False  \n",
+       "6        0    False     False  False     True     False  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_oversampled = pd.concat([no_fraud,yes_fraud_oversampled])\n",
+    "train_oversampled.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(199768, 12)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_oversampled.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train_over = train_oversampled['isFraud'].copy()\n",
+    "X_train_over = train_oversampled.drop('isFraud',axis = 1).copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "LR = LogisticRegression(max_iter=1000)\n",
+    "LR.fit(X_train_over, y_train_over)\n",
+    "pred = LR.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[16992,  2986],\n",
+       "       [    1,    21]], dtype=int64)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "confusion_matrix(y_test,pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      0.85      0.92     19978\n",
+      "           1       0.01      0.95      0.01        22\n",
+      "\n",
+      "    accuracy                           0.85     20000\n",
+      "   macro avg       0.50      0.90      0.47     20000\n",
+      "weighted avg       1.00      0.85      0.92     20000\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(y_test,pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lower Accuracy: The accuracy is 0.85, which is lower than the first model. However, this model is significantly better at identifying fraud (class 1).\n",
+    "Recall (0.96 for class 1): This model successfully identifies 96% of actual fraud cases. The model's recall for class 1 (fraud) is significantly higher, meaning it is much better at catching fraudulent transactions.\n",
+    "Precision (0.01 for class 1): The precision for class 1 is low (0.01), indicating that when the model predicts a transaction as fraudulent, it is often incorrect. However, in fraud detection, a higher recall is generally more important because missing fraudulent transactions (low recall) can be very costly.\n",
+    "F1-Score (0.01 for class 1): The F1-score for class 1 is low, reflecting the trade-off between precision and recall. However, the high recall suggests that this model is much more useful in scenarios where identifying fraud is critical."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Undersampling:\n",
+    "# undersample majority\n",
+    "no_fraud_undersampled = resample(no_fraud, #<- downsample from here\n",
+    "                                    replace=False, #<- no need to reuse data now, we have an abundance\n",
+    "                                    n_samples = len(fraud),\n",
+    "                                    random_state=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(116, 12)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(116, 12)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(fraud.shape)\n",
+    "display(no_fraud_undersampled.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "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>amount</th>\n",
+       "      <th>nameOrig</th>\n",
+       "      <th>newbalanceOrig</th>\n",
+       "      <th>nameDest</th>\n",
+       "      <th>newbalanceDest</th>\n",
+       "      <th>isFraud</th>\n",
+       "      <th>CASH_IN</th>\n",
+       "      <th>CASH_OUT</th>\n",
+       "      <th>DEBIT</th>\n",
+       "      <th>PAYMENT</th>\n",
+       "      <th>TRANSFER</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>15636</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9303</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>91777</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8273</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2806.0</td>\n",
+       "      <td>21581</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11864</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2806.0</td>\n",
+       "      <td>56833</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>53</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>680</th>\n",
+       "      <td>1</td>\n",
+       "      <td>20128.0</td>\n",
+       "      <td>19251</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5325</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     step   amount  nameOrig  newbalanceOrig  nameDest  newbalanceDest  \\\n",
+       "2       1    181.0     15636             0.0      9303             0.0   \n",
+       "3       1    181.0     91777             0.0      8273             0.0   \n",
+       "251     1   2806.0     21581             0.0     11864             0.0   \n",
+       "252     1   2806.0     56833             0.0        53             0.0   \n",
+       "680     1  20128.0     19251             0.0      5325             0.0   \n",
+       "\n",
+       "     isFraud  CASH_IN  CASH_OUT  DEBIT  PAYMENT  TRANSFER  \n",
+       "2          1    False     False  False    False      True  \n",
+       "3          1    False      True  False    False     False  \n",
+       "251        1    False     False  False    False      True  \n",
+       "252        1    False      True  False    False     False  \n",
+       "680        1    False     False  False    False      True  "
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_undersampled = pd.concat([fraud,no_fraud_undersampled])\n",
+    "train_undersampled.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train_under = train_undersampled['isFraud'].copy()\n",
+    "X_train_under = train_undersampled.drop('isFraud',axis = 1).copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "LR = LogisticRegression(max_iter=1000)\n",
+    "LR.fit(X_train_under, y_train_under)\n",
+    "pred = LR.predict(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[16854,  3124],\n",
+       "       [    1,    21]], dtype=int64)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "confusion_matrix(y_test,pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      0.84      0.92     19978\n",
+      "           1       0.01      0.95      0.01        22\n",
+      "\n",
+      "    accuracy                           0.84     20000\n",
+      "   macro avg       0.50      0.90      0.46     20000\n",
+      "weighted avg       1.00      0.84      0.91     20000\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(y_test,pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Using SMOTE\n",
+    "from imblearn.over_sampling import SMOTE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sm = SMOTE(random_state = 42,sampling_strategy=1.0)\n",
+    "X_train_SMOTE,y_train_SMOTE = sm.fit_resample(X_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "LR = LogisticRegression(max_iter=1000)\n",
+    "LR.fit(X_train_SMOTE, y_train_SMOTE)\n",
+    "pred = LR.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[18559,  1419],\n",
+       "       [    1,    21]], dtype=int64)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "confusion_matrix(y_test,pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      0.93      0.96     19978\n",
+      "           1       0.01      0.95      0.03        22\n",
+      "\n",
+      "    accuracy                           0.93     20000\n",
+      "   macro avg       0.51      0.94      0.50     20000\n",
+      "weighted avg       1.00      0.93      0.96     20000\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(y_test,pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "SMOTE with another models:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Random Forest Classifier with SMOTE\n",
+      "[[19939    39]\n",
+      " [   13     9]]\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00     19978\n",
+      "           1       0.19      0.41      0.26        22\n",
+      "\n",
+      "    accuracy                           1.00     20000\n",
+      "   macro avg       0.59      0.70      0.63     20000\n",
+      "weighted avg       1.00      1.00      1.00     20000\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "classifier = RandomForestClassifier(random_state=42)\n",
+    "classifier.fit(X_train_SMOTE, y_train_SMOTE)\n",
+    "\n",
+    "\n",
+    "y_pred = classifier.predict(X_test)\n",
+    "\n",
+    "print(\"Random Forest Classifier with SMOTE\")\n",
+    "print(confusion_matrix(y_test, y_pred))\n",
+    "print(classification_report(y_test, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from xgboost import XGBClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Yuliya Lavrenyuk\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\core.py:158: UserWarning: [10:06:03] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
+      "Parameters: { \"use_label_encoder\" } are not used.\n",
+      "\n",
+      "  warnings.warn(smsg, UserWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "XGBoost Classifier with SMOTE\n",
+      "[[19861   117]\n",
+      " [   12    10]]\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      0.99      1.00     19978\n",
+      "           1       0.08      0.45      0.13        22\n",
+      "\n",
+      "    accuracy                           0.99     20000\n",
+      "   macro avg       0.54      0.72      0.57     20000\n",
+      "weighted avg       1.00      0.99      1.00     20000\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "xgb_model = XGBClassifier(use_label_encoder=False, eval_metric='logloss', random_state=42)\n",
+    "xgb_model.fit(X_train_SMOTE, y_train_SMOTE)\n",
+    "\n",
+    "# Step 3: Predict on the test data\n",
+    "y_pred = xgb_model.predict(X_test)\n",
+    "\n",
+    "# Step 4: Evaluate the model\n",
+    "print(\"XGBoost Classifier with SMOTE\")\n",
+    "print(confusion_matrix(y_test, y_pred))\n",
+    "print(classification_report(y_test, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can try to use grid search and improve accuracy:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import GridSearchCV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 3 folds for each of 108 candidates, totalling 324 fits\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Yuliya Lavrenyuk\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\core.py:158: UserWarning: [10:17:22] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
+      "Parameters: { \"use_label_encoder\" } are not used.\n",
+      "\n",
+      "  warnings.warn(smsg, UserWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "param_grid = {\n",
+    "    'max_depth': [3, 5, 7],\n",
+    "    'learning_rate': [0.01, 0.1, 0.2],\n",
+    "    'n_estimators': [100, 200, 500],\n",
+    "    'subsample': [0.8, 1.0],\n",
+    "    'colsample_bytree': [0.8, 1.0],\n",
+    "}\n",
+    "\n",
+    "grid_search = GridSearchCV(estimator=XGBClassifier(use_label_encoder=False, eval_metric='logloss', random_state=42),\n",
+    "                           param_grid=param_grid, \n",
+    "                           scoring='f1', \n",
+    "                           cv=3, \n",
+    "                           verbose=1, \n",
+    "                           n_jobs=-1)\n",
+    "\n",
+    "grid_search.fit(X_train_SMOTE, y_train_SMOTE)\n",
+    "\n",
+    "best_model = grid_search.best_estimator_\n",
+    "\n",
+    "# # Predict and evaluate with the best model\n",
+    "# y_pred = best_model.predict(X_test)\n",
+    "# print(confusion_matrix(y_test, y_pred))\n",
+    "# print(classification_report(y_test, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'colsample_bytree': 1.0,\n",
+       " 'learning_rate': 0.2,\n",
+       " 'max_depth': 7,\n",
+       " 'n_estimators': 500,\n",
+       " 'subsample': 0.8}"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid_search.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Yuliya Lavrenyuk\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\xgboost\\core.py:158: UserWarning: [10:17:36] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
+      "Parameters: { \"use_label_encoder\" } are not used.\n",
+      "\n",
+      "  warnings.warn(smsg, UserWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "XGBoost Classifier with SMOTE\n",
+      "[[19908    70]\n",
+      " [   13     9]]\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00     19978\n",
+      "           1       0.11      0.41      0.18        22\n",
+      "\n",
+      "    accuracy                           1.00     20000\n",
+      "   macro avg       0.56      0.70      0.59     20000\n",
+      "weighted avg       1.00      1.00      1.00     20000\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "xgb_model = XGBClassifier(use_label_encoder=True, eval_metric='logloss', random_state=42, colsample_bytree = 1.0,\n",
+    " learning_rate=0.2,\n",
+    " max_depth= 7,\n",
+    " n_estimators= 500,\n",
+    " subsample=0.8)\n",
+    "\n",
+    "xgb_model.fit(X_train_SMOTE, y_train_SMOTE)\n",
+    "\n",
+    "# Step 3: Predict on the test data\n",
+    "y_pred = xgb_model.predict(X_test)\n",
+    "\n",
+    "# Step 4: Evaluate the model\n",
+    "print(\"XGBoost Classifier with SMOTE\")\n",
+    "print(confusion_matrix(y_test, y_pred))\n",
+    "print(classification_report(y_test, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Which model worked better and how do you know?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "unterminated string literal (detected at line 7) (2272469407.py, line 7)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;36m  Cell \u001b[1;32mIn[52], line 7\u001b[1;36m\u001b[0m\n\u001b[1;33m    Model Complexity: XGBoost is more complex than linear classifiers, which means it might require more careful tuning of hyperparameters, particularly with imbalanced data. If the model isn't well-tuned, it can perform worse than simpler models.\u001b[0m\n\u001b[1;37m                                                                                                                                                                                               ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m unterminated string literal (detected at line 7)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Your response here\n",
+    "In my results, it seems that the XGBoost model and RF, despite being a more sophisticated and powerful models, are performing worse than a linear classifier.\n",
+    "This may happen for several reasons:\n",
+    "\n",
+    "Overfitting: XGBoost is a highly flexible model with many hyperparameters. Without careful tuning, it can easily overfit to the training data, especially when using techniques like SMOTE, which can sometimes introduce noise by generating synthetic data points.\n",
+    "\n",
+    "Model Complexity: XGBoost is more complex than linear classifiers, which means it might require more careful tuning of hyperparameters, particularly with imbalanced data. If the model isn't well-tuned, it can perform worse than simpler models.\n",
+    "\n",
+    "Data Quality: The quality and characteristics of the data after applying SMOTE can sometimes degrade the performance of more complex models. SMOTE generates synthetic samples, which might not always perfectly represent real data, especially in high-dimensional spaces.\n",
+    "\n",
+    "Class Imbalance: Even with SMOTE, if the classes are still imbalanced, it could cause issues. Additionally, SMOTE sometimes introduces synthetic points that might not align well with the decision boundaries needed by more complex models like XGBoost.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Note: before doing the first commit, make sure you don't include the large csv file, either by adding it to .gitignore, or by deleting it."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}