diff --git a/your-code/challenge1.ipynb b/your-code/challenge1.ipynb
index 6f32890..58e58d6 100644
--- a/your-code/challenge1.ipynb
+++ b/your-code/challenge1.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -8,6 +9,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -20,6 +22,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -41,6 +44,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -52,30 +56,76 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here.\n"
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import linear_model \n"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "Draw a dispersion diagram (scatter plot) for the data."
+    "pip install statsmodels"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your code here.\n"
+    "# Your code here.\n",
+    "student_data = pd.DataFrame({'X': [17,51,27,21,36,48,19,26,54,30], 'Y': [10,1,5,9,4,2,9,6,0,3]})"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Draw a dispersion diagram (scatter plot) for the data."
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x22e22f7a8d0>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Your code here.\n",
+    "plt.scatter(student_data[\"X\"],student_data[\"Y\"])"
+   ]
+  },
+  {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -84,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,6 +142,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -100,18 +151,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          X         Y\n",
+      "X  1.000000 -0.939163\n",
+      "Y -0.939163  1.000000\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[187.65555556, -45.56666667],\n",
+       "       [-45.56666667,  12.54444444]])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Your response here.\n",
     "#correlation:\n",
+    "print(student_data.corr())\n",
     "\n",
-    "\n",
-    "#covariance:\n"
+    "#covariance:\n",
+    "np.cov(student_data[\"X\"],student_data[\"Y\"])"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -120,14 +194,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your response here.\n"
+    "# Your response here.\n",
+    "x = student_data[[\"X\"]] \n",
+    "y = student_data[\"Y\"]\n",
+    "model = linear_model.LinearRegression()\n",
+    "result = model.fit(x,y)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -136,14 +215,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here.\n"
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x22e22ffbb90>]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Your code here.\n",
+    "plt.scatter(student_data[\"X\"],student_data[\"Y\"])\n",
+    "\n",
+    "\n",
+    "regression_line = result.intercept_ + result.coef_[0]*x\n",
+    "plt.plot(x, regression_line, color = \"green\")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -152,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,6 +266,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -169,14 +276,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Import any libraries you may need & the data\n"
+    "# Import any libraries you may need & the data\n",
+    "vehicles = pd.read_csv(\"vehicles.csv\")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -187,14 +296,130 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your response here. \n"
+   "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>Year</th>\n",
+       "      <th>Cylinders</th>\n",
+       "      <th>Fuel Barrels/Year</th>\n",
+       "      <th>Combined MPG</th>\n",
+       "      <th>Fuel Cost/Year</th>\n",
+       "      <th>CO2 Emission Grams/Mile</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Year</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.082469</td>\n",
+       "      <td>-0.221084</td>\n",
+       "      <td>0.204751</td>\n",
+       "      <td>-0.091913</td>\n",
+       "      <td>-0.222300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cylinders</th>\n",
+       "      <td>0.082469</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.739517</td>\n",
+       "      <td>-0.698648</td>\n",
+       "      <td>0.778153</td>\n",
+       "      <td>0.752393</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fuel Barrels/Year</th>\n",
+       "      <td>-0.221084</td>\n",
+       "      <td>0.739517</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.909743</td>\n",
+       "      <td>0.916208</td>\n",
+       "      <td>0.986189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Combined MPG</th>\n",
+       "      <td>0.204751</td>\n",
+       "      <td>-0.698648</td>\n",
+       "      <td>-0.909743</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.875185</td>\n",
+       "      <td>-0.926229</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fuel Cost/Year</th>\n",
+       "      <td>-0.091913</td>\n",
+       "      <td>0.778153</td>\n",
+       "      <td>0.916208</td>\n",
+       "      <td>-0.875185</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.930865</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CO2 Emission Grams/Mile</th>\n",
+       "      <td>-0.222300</td>\n",
+       "      <td>0.752393</td>\n",
+       "      <td>0.986189</td>\n",
+       "      <td>-0.926229</td>\n",
+       "      <td>0.930865</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             Year  Cylinders  Fuel Barrels/Year  Combined MPG   \n",
+       "Year                     1.000000   0.082469          -0.221084      0.204751  \\\n",
+       "Cylinders                0.082469   1.000000           0.739517     -0.698648   \n",
+       "Fuel Barrels/Year       -0.221084   0.739517           1.000000     -0.909743   \n",
+       "Combined MPG             0.204751  -0.698648          -0.909743      1.000000   \n",
+       "Fuel Cost/Year          -0.091913   0.778153           0.916208     -0.875185   \n",
+       "CO2 Emission Grams/Mile -0.222300   0.752393           0.986189     -0.926229   \n",
+       "\n",
+       "                         Fuel Cost/Year  CO2 Emission Grams/Mile  \n",
+       "Year                          -0.091913                -0.222300  \n",
+       "Cylinders                      0.778153                 0.752393  \n",
+       "Fuel Barrels/Year              0.916208                 0.986189  \n",
+       "Combined MPG                  -0.875185                -0.926229  \n",
+       "Fuel Cost/Year                 1.000000                 0.930865  \n",
+       "CO2 Emission Grams/Mile        0.930865                 1.000000  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your response here. \n",
+    "vehicles_2 = vehicles[[\"Year\",\"Cylinders\",\"Fuel Barrels/Year\", \"Combined MPG\", \"Fuel Cost/Year\",\"CO2 Emission Grams/Mile\"]]\n",
+    "vehicles_2.corr()\n",
+    "\n",
+    "#Fuel Barrels per year"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -203,14 +428,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your response here. \n"
-   ]
-  },
-  {
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9802919387898396"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your response here. \n",
+    "x = vehicles_2[[\"Cylinders\",\"Fuel Barrels/Year\",\"Fuel Cost/Year\",\"Combined MPG\"]] \n",
+    "y = vehicles_2[\"CO2 Emission Grams/Mile\"]\n",
+    "model = linear_model.LinearRegression()\n",
+    "result = model.fit(x,y)\n",
+    "result.score(x,y)"
+   ]
+  },
+  {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -219,14 +461,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your response here. \n"
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "131.1563247541926\n",
+      "[ 1.04483633 19.62713308  0.029393   -3.16658852]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.9802919387898396"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your response here. \n",
+    "print(model.intercept_) \n",
+    "print(model.coef_)\n",
+    "result.score(x,y)\n",
+    "\n",
+    "# The most important values are the one with a tring correlation as we can see the score of the model is 98. so 98 of time it outputs the correct prediction"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -234,6 +501,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -252,6 +520,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -261,14 +530,104 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here.\n"
-   ]
-  },
-  {
+   "execution_count": 65,
+   "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>X</th>\n",
+       "      <th>Y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>8</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>9</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>14</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    X   Y\n",
+       "0   1   1\n",
+       "1   3   2\n",
+       "2   4   4\n",
+       "3   6   4\n",
+       "4   8   5\n",
+       "5   9   7\n",
+       "6  11   8\n",
+       "7  14  13"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here.\n",
+    "party = pd.DataFrame({'X': [1,3,4,6,8,9,11,14], 'Y': [1,2,4,4,5,7,8,13]})\n",
+    "party"
+   ]
+  },
+  {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -277,14 +636,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here.\n"
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x22e2ca899d0>"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Your code here.\n",
+    "x = party[[\"X\"]] \n",
+    "y = party[\"Y\"]\n",
+    "model = linear_model.LinearRegression()\n",
+    "result = model.fit(x,y)\n",
+    "model.coef_\n",
+    "model.intercept_\n",
+    "\n",
+    "linear = model.intercept_ + model.coef_[0]*x\n",
+    "plt.plot(x,linear)\n",
+    "plt.scatter(x,y)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -293,14 +684,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your response here. \n"
+    "# Your response here. \n",
+    "\n",
+    "#There is a strong correlation beetwen variables"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -309,14 +703,138 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your response here. \n"
+    "# Your response here. \n",
+    "party.drop([4,7], axis=0, inplace=True)"
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 71,
+   "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>X</th>\n",
+       "      <th>Y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>9</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    X  Y\n",
+       "0   1  1\n",
+       "1   3  2\n",
+       "2   4  4\n",
+       "3   6  4\n",
+       "5   9  7\n",
+       "6  11  8"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "party2 = party.copy()\n",
+    "party2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x22e2cbe9990>"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = party[[\"X\"]] \n",
+    "y = party[\"Y\"]\n",
+    "model=linear_model.LinearRegression()\n",
+    "model.fit(party2[[\"X\"]], party2[\"Y\"])\n",
+    "\n",
+    "linear2 = model.intercept_ + model.coef_[0]*x\n",
+    "plt.plot(x,linear2)\n",
+    "plt.scatter(x,y)"
+   ]
+  },
+  {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -325,11 +843,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Your response here. \n"
+    "# Your response here. \n",
+    "# it moved the slope of the regression line\n"
    ]
   }
  ],
@@ -349,7 +868,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.11.2"
   }
  },
  "nbformat": 4,