diff --git a/assignment1_plot_beer_consumption.ipynb b/assignment1_plot_beer_consumption.ipynb
new file mode 100644
index 0000000..b6921ec
--- /dev/null
+++ b/assignment1_plot_beer_consumption.ipynb
@@ -0,0 +1,147 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "90956aae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e8d9b2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    Year  WO [x1000]  NL Beer consumption [x1000 hectoliter]\n",
+      "0   2006       205.9                                   11402\n",
+      "1   2007       208.6                                   11492\n",
+      "2   2008       212.7                                   11450\n",
+      "3   2009       220.5                                   11502\n",
+      "4   2010       233.2                                   11474\n",
+      "5   2011       242.4                                   11480\n",
+      "6   2012       245.4                                   11452\n",
+      "7   2013       241.4                                   11484\n",
+      "8   2014       250.2                                   11555\n",
+      "9   2015       255.7                                   11601\n",
+      "10  2016       261.2                                   11731\n",
+      "11  2017       267.9                                   11862\n",
+      "12  2018       280.1                                   12048\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('istherecorrelation.csv', sep=\";\")\n",
+    "data.columns = data.columns.str.strip()\n",
+    "data[\"WO [x1000]\"] = (\n",
+    "    data[\"WO [x1000]\"]\n",
+    "    .astype(str)\n",
+    "    .str.replace(\",\", \".\", regex=False)\n",
+    "    .astype(float)\n",
+    ")\n",
+    "print(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34aa9f9b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0     55.376396\n",
+      "1     55.091083\n",
+      "2     53.831688\n",
+      "3     52.163265\n",
+      "4     49.202401\n",
+      "5     47.359736\n",
+      "6     46.666667\n",
+      "7     47.572494\n",
+      "8     46.183054\n",
+      "9     45.369574\n",
+      "10    44.911945\n",
+      "11    44.277716\n",
+      "12    43.013210\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "year = data[\"Year\"]\n",
+    "num_students = data[\"WO [x1000]\"]\n",
+    "beer = data[\"NL Beer consumption [x1000 hectoliter]\"]\n",
+    "\n",
+    "beer_per_student = beer / num_students\n",
+    "print(beer_per_student)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "8ae2ace5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 3000x1800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6), dpi=300)\n",
+    "plt.plot(year, beer_per_student, marker=\"o\", linestyle=\"-\", color=\"hotpink\")\n",
+    "\n",
+    "plt.title(\"Average Yearly Beer Consumption per University Student in the Netherlands\")\n",
+    "plt.xlabel(\"Year\")\n",
+    "plt.ylabel(\"Beer per Student (liters)\")\n",
+    "plt.grid(True, linestyle=\":\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25d4d563",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/plot_beer_consumption.png b/plot_beer_consumption.png
new file mode 100644
index 0000000..a060041
Binary files /dev/null and b/plot_beer_consumption.png differ
diff --git a/solution_.md b/solution_.md
new file mode 100644
index 0000000..8c8df3b
--- /dev/null
+++ b/solution_.md
@@ -0,0 +1,9 @@
+13696475
+
+Fantastic yeasts and where to find them: the hidden diversity of dimorphic fungal pathogens <br>
+An analysis of the forces required to drag sheep over various surfaces <br>
+The neurocognitive effects of alcohol on adolescents and college students
+
+![Plot of average yearly beer consumption per student](plot_beer_consumption.png)
+
+The graph shows the average yearly beer consumption per university student in the Netherlands from 2006 until 2018. In the year 2006 the average beer consumption is at about 55 liters per student. Over the years, the beer consumption decreased, reaching approximatly 43 liters per student in 2018.