diff --git a/lab_imbalance.ipynb b/lab_imbalance.ipynb
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+{
+ "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": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n = 100000 \n",
+    "fraud = pd.read_csv('C:/Users/ACER/Desktop/Data Analysis/Labs/lab-imbalance/your-code/Fraud.csv', nrows=n)"
+   ]
+  },
+  {
+   "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>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>170136.00</td>\n",
+       "      <td>160296.36</td>\n",
+       "      <td>M1979787155</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</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>21249.00</td>\n",
+       "      <td>19384.72</td>\n",
+       "      <td>M2044282225</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</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>181.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>C553264065</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</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>181.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>C38997010</td>\n",
+       "      <td>21182.0</td>\n",
+       "      <td>0.00</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>41554.00</td>\n",
+       "      <td>29885.86</td>\n",
+       "      <td>M1230701703</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>7817.71</td>\n",
+       "      <td>C90045638</td>\n",
+       "      <td>53860.00</td>\n",
+       "      <td>46042.29</td>\n",
+       "      <td>M573487274</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>7107.77</td>\n",
+       "      <td>C154988899</td>\n",
+       "      <td>183195.00</td>\n",
+       "      <td>176087.23</td>\n",
+       "      <td>M408069119</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>7861.64</td>\n",
+       "      <td>C1912850431</td>\n",
+       "      <td>176087.23</td>\n",
+       "      <td>168225.59</td>\n",
+       "      <td>M633326333</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1</td>\n",
+       "      <td>PAYMENT</td>\n",
+       "      <td>4024.36</td>\n",
+       "      <td>C1265012928</td>\n",
+       "      <td>2671.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>M1176932104</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "      <td>DEBIT</td>\n",
+       "      <td>5337.77</td>\n",
+       "      <td>C712410124</td>\n",
+       "      <td>41720.00</td>\n",
+       "      <td>36382.23</td>\n",
+       "      <td>C195600860</td>\n",
+       "      <td>41898.0</td>\n",
+       "      <td>40348.79</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      170136.00       160296.36   \n",
+       "1     1   PAYMENT   1864.28  C1666544295       21249.00        19384.72   \n",
+       "2     1  TRANSFER    181.00  C1305486145         181.00            0.00   \n",
+       "3     1  CASH_OUT    181.00   C840083671         181.00            0.00   \n",
+       "4     1   PAYMENT  11668.14  C2048537720       41554.00        29885.86   \n",
+       "5     1   PAYMENT   7817.71    C90045638       53860.00        46042.29   \n",
+       "6     1   PAYMENT   7107.77   C154988899      183195.00       176087.23   \n",
+       "7     1   PAYMENT   7861.64  C1912850431      176087.23       168225.59   \n",
+       "8     1   PAYMENT   4024.36  C1265012928        2671.00            0.00   \n",
+       "9     1     DEBIT   5337.77   C712410124       41720.00        36382.23   \n",
+       "\n",
+       "      nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
+       "0  M1979787155             0.0            0.00        0               0  \n",
+       "1  M2044282225             0.0            0.00        0               0  \n",
+       "2   C553264065             0.0            0.00        1               0  \n",
+       "3    C38997010         21182.0            0.00        1               0  \n",
+       "4  M1230701703             0.0            0.00        0               0  \n",
+       "5   M573487274             0.0            0.00        0               0  \n",
+       "6   M408069119             0.0            0.00        0               0  \n",
+       "7   M633326333             0.0            0.00        0               0  \n",
+       "8  M1176932104             0.0            0.00        0               0  \n",
+       "9   C195600860         41898.0        40348.79        0               0  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fraud.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(100000, 11)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fraud.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fraud.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "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": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fraud.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.countplot(data=fraud, x='type')\n",
+    "plt.xlabel('Type of payment')\n",
+    "plt.ylabel('Amount')\n",
+    "plt.title('Comparison of types of payment')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "correlation_matrix = fraud.corr()\n",
+    "\n",
+    "# Plot heatmap of correlation matrix\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0)\n",
+    "plt.title('Correlation Heatmap')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What is the distribution of the outcome? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Your response here\n",
+    "outcome = \"isFraud\"\n",
+    "\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.hist(fraud[outcome], bins=20, edgecolor='black')\n",
+    "plt.xlabel('Outcome')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.title('Distribution of Outcome')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "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": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here\n",
+    "fraud.drop(columns=['nameDest', 'nameOrig'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dummies = pd.get_dummies(fraud['type'], prefix='type', drop_first=True)\n",
+    "\n",
+    "fraud_dummified = pd.concat([fraud, dummies], axis=1)\n",
+    "\n",
+    "fraud_dummified.drop('type', axis=1, inplace=True)"
+   ]
+  },
+  {
+   "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>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",
+       "      <th>type_CASH_OUT</th>\n",
+       "      <th>type_DEBIT</th>\n",
+       "      <th>type_PAYMENT</th>\n",
+       "      <th>type_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>170136.0</td>\n",
+       "      <td>160296.36</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1864.28</td>\n",
+       "      <td>21249.0</td>\n",
+       "      <td>19384.72</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>181.00</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>181.00</td>\n",
+       "      <td>181.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>21182.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>11668.14</td>\n",
+       "      <td>41554.0</td>\n",
+       "      <td>29885.86</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</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>159929.0</td>\n",
+       "      <td>155908.34</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99996</th>\n",
+       "      <td>10</td>\n",
+       "      <td>18345.49</td>\n",
+       "      <td>6206.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99997</th>\n",
+       "      <td>10</td>\n",
+       "      <td>183774.91</td>\n",
+       "      <td>39173.0</td>\n",
+       "      <td>222947.91</td>\n",
+       "      <td>54925.05</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99998</th>\n",
+       "      <td>10</td>\n",
+       "      <td>82237.17</td>\n",
+       "      <td>6031.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>592635.66</td>\n",
+       "      <td>799140.46</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99999</th>\n",
+       "      <td>10</td>\n",
+       "      <td>20096.56</td>\n",
+       "      <td>110117.0</td>\n",
+       "      <td>90020.44</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100000 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       step     amount  oldbalanceOrg  newbalanceOrig  oldbalanceDest  \\\n",
+       "0         1    9839.64       170136.0       160296.36            0.00   \n",
+       "1         1    1864.28        21249.0        19384.72            0.00   \n",
+       "2         1     181.00          181.0            0.00            0.00   \n",
+       "3         1     181.00          181.0            0.00        21182.00   \n",
+       "4         1   11668.14        41554.0        29885.86            0.00   \n",
+       "...     ...        ...            ...             ...             ...   \n",
+       "99995    10    4020.66       159929.0       155908.34            0.00   \n",
+       "99996    10   18345.49         6206.0            0.00            0.00   \n",
+       "99997    10  183774.91        39173.0       222947.91        54925.05   \n",
+       "99998    10   82237.17         6031.0            0.00       592635.66   \n",
+       "99999    10   20096.56       110117.0        90020.44            0.00   \n",
+       "\n",
+       "       newbalanceDest  isFraud  isFlaggedFraud  type_CASH_OUT  type_DEBIT  \\\n",
+       "0                0.00        0               0              0           0   \n",
+       "1                0.00        0               0              0           0   \n",
+       "2                0.00        1               0              0           0   \n",
+       "3                0.00        1               0              1           0   \n",
+       "4                0.00        0               0              0           0   \n",
+       "...               ...      ...             ...            ...         ...   \n",
+       "99995            0.00        0               0              0           0   \n",
+       "99996            0.00        0               0              0           0   \n",
+       "99997            0.00        0               0              0           0   \n",
+       "99998       799140.46        0               0              1           0   \n",
+       "99999            0.00        0               0              0           0   \n",
+       "\n",
+       "       type_PAYMENT  type_TRANSFER  \n",
+       "0                 1              0  \n",
+       "1                 1              0  \n",
+       "2                 0              1  \n",
+       "3                 0              0  \n",
+       "4                 1              0  \n",
+       "...             ...            ...  \n",
+       "99995             1              0  \n",
+       "99996             1              0  \n",
+       "99997             0              0  \n",
+       "99998             0              0  \n",
+       "99999             1              0  \n",
+       "\n",
+       "[100000 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fraud_dummified"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run a logisitc regression classifier and evaluate its accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9989\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "X = fraud_dummified.drop('isFraud', axis=1)\n",
+    "y = fraud_dummified['isFraud']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "logreg_model = LogisticRegression()\n",
+    "\n",
+    "# Train the model on the training data\n",
+    "logreg_model.fit(X_train, y_train)\n",
+    "\n",
+    "# Make predictions on the test data\n",
+    "y_pred = logreg_model.predict(X_test)\n",
+    "\n",
+    "# Calculate accuracy\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "\n",
+    "accuracy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now pick a model of your choice and evaluate its accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.99965"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "import xgboost as xgb\n",
+    "xgb_model = xgb.XGBClassifier()\n",
+    "\n",
+    "xgb_model.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = xgb_model.predict(X_test)\n",
+    "\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "\n",
+    "accuracy\n"
+   ]
+  },
+  {
+   "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\n",
+    "#XGBoost model worked better as the accuracy score was 0.99965 for xgb and 0.9989 for logistic regression\n",
+    "#meaning that xgboost had 0.0007 higher accuracy, which is very little, but still better"
+   ]
+  },
+  {
+   "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": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}