diff --git a/your-code/lab_imbalance.ipynb b/your-code/lab_imbalance.ipynb
index dbb15e1..bfb02a0 100644
--- a/your-code/lab_imbalance.ipynb
+++ b/your-code/lab_imbalance.ipynb
@@ -1,147 +1,956 @@
-{
- "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": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(\"C:/Users/Vladimir/Desktop/Ironhack_labs/Week18/lab-imbalance/your-code/Fraud.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = data.head(100000)"
+   ]
+  },
+  {
+   "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>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>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",
+       "    </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",
+       "    </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",
+       "    </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",
+       "    </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",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       step      type     amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\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",
+       "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  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "step                int64\n",
+       "type               object\n",
+       "amount            float64\n",
+       "nameOrig           object\n",
+       "oldbalanceOrg     float64\n",
+       "newbalanceOrig    float64\n",
+       "nameDest           object\n",
+       "oldbalanceDest    float64\n",
+       "newbalanceDest    float64\n",
+       "isFraud             int64\n",
+       "isFlaggedFraud      int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.dtypes"
+   ]
+  },
+  {
+   "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>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": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['PAYMENT', 'TRANSFER', 'CASH_OUT', 'DEBIT', 'CASH_IN'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"type\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    100000\n",
+       "Name: isFlaggedFraud, dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['isFlaggedFraud'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    99884\n",
+       "1      116\n",
+       "Name: isFraud, dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['isFraud'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What is the distribution of the outcome? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.hist(data[\"isFraud\"], bins=20, edgecolor='black')\n",
+    "plt.xlabel(\"isFraud\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.title(\"Distribution of Frauds\")\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "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": 19,
+   "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  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.drop(\"nameOrig\", axis = 1, inplace = True)\n",
+    "data.drop(\"nameDest\", axis = 1, inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "step                int64\n",
+       "type               object\n",
+       "amount            float64\n",
+       "oldbalanceOrg     float64\n",
+       "newbalanceOrig    float64\n",
+       "oldbalanceDest    float64\n",
+       "newbalanceDest    float64\n",
+       "isFraud             int64\n",
+       "isFlaggedFraud      int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "step              0\n",
+       "type              0\n",
+       "amount            0\n",
+       "oldbalanceOrg     0\n",
+       "newbalanceOrig    0\n",
+       "oldbalanceDest    0\n",
+       "newbalanceDest    0\n",
+       "isFraud           0\n",
+       "isFlaggedFraud    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dummy = pd.get_dummies(data, columns=['type'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_dummy['step'].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dummy.drop(\"isFlaggedFraud\", axis = 1, inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Vladimir\\AppData\\Local\\Temp\\ipykernel_13048\\2537461712.py:5: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  mask = np.zeros_like(corr, dtype=np.bool)\n"
+     ]
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 1400x1400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sn\n",
+    "\n",
+    "corr=np.abs(data_dummy.corr())\n",
+    "\n",
+    "mask = np.zeros_like(corr, dtype=np.bool)\n",
+    "mask[np.triu_indices_from(mask)] = True\n",
+    "\n",
+    "\n",
+    "f, ax = plt.subplots(figsize=(14, 14))\n",
+    "\n",
+    "cmap = sn.diverging_palette(220, 10, as_cmap=True)\n",
+    "\n",
+    "sn.heatmap(corr, mask=mask,  vmax=1,square=True, linewidths=.5, cbar_kws={\"shrink\": .5},annot = corr)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dummy.drop(\"oldbalanceDest\", axis = 1, inplace = True)\n",
+    "data_dummy.drop(\"oldbalanceOrg\", axis = 1, inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Vladimir\\AppData\\Local\\Temp\\ipykernel_13048\\2537461712.py:5: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  mask = np.zeros_like(corr, dtype=np.bool)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1400x1400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sn\n",
+    "\n",
+    "corr=np.abs(data_dummy.corr())\n",
+    "\n",
+    "mask = np.zeros_like(corr, dtype=np.bool)\n",
+    "mask[np.triu_indices_from(mask)] = True\n",
+    "\n",
+    "\n",
+    "f, ax = plt.subplots(figsize=(14, 14))\n",
+    "\n",
+    "cmap = sn.diverging_palette(220, 10, as_cmap=True)\n",
+    "\n",
+    "sn.heatmap(corr, mask=mask,  vmax=1,square=True, linewidths=.5, cbar_kws={\"shrink\": .5},annot = corr)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run a logisitc regression classifier and evaluate its accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9987\n"
+     ]
+    }
+   ],
+   "source": [
+    "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 = data_dummy.drop(columns=[\"isFraud\"])  \n",
+    "y = data_dummy[\"isFraud\"]\n",
+    "\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 = LogisticRegression()\n",
+    "\n",
+    "logreg.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = logreg.predict(X_test)\n",
+    "\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "\n",
+    "print(accuracy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now pick a model of your choice and evaluate its accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9992\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "rf_classifier = RandomForestClassifier(random_state=42)\n",
+    "\n",
+    "\n",
+    "rf_classifier.fit(X_train, y_train)\n",
+    "\n",
+    "\n",
+    "y_pred = rf_classifier.predict(X_test)\n",
+    "\n",
+    "\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "\n",
+    "print(accuracy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Which model worked better and how do you know?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#the better accuracy score has random forrest, but its too high that is suspicious. Im sure I made a mistake somewhere."
+   ]
+  },
+  {
+   "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 (ipykernel)",
+   "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
+}