diff --git a/analysis.ipynb b/analysis.ipynb
new file mode 100644
index 0000000..7a9fb44
--- /dev/null
+++ b/analysis.ipynb
@@ -0,0 +1,219 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Year</th>\n",
+       "      <th>WO [x1000]</th>\n",
+       "      <th>NL Beer consumption [x1000 hectoliter]</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2006</td>\n",
+       "      <td>205,9</td>\n",
+       "      <td>11402</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2007</td>\n",
+       "      <td>208,6</td>\n",
+       "      <td>11492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2008</td>\n",
+       "      <td>212,7</td>\n",
+       "      <td>11450</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   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"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(\"istherecorrelation.csv\", sep=\";\")\n",
+    "data.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "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>WO [x1000]</th>\n",
+       "      <th>NL Beer consumption [x1000 hectoliter]</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2006</td>\n",
+       "      <td>205.9</td>\n",
+       "      <td>11402</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2007</td>\n",
+       "      <td>208.6</td>\n",
+       "      <td>11492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2008</td>\n",
+       "      <td>212.7</td>\n",
+       "      <td>11450</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   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"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"WO [x1000]\"].replace({r\",\": \".\"}, inplace=True, regex=True)\n",
+    "data[\"WO [x1000]\"] = pd.to_numeric(data[\"WO [x1000]\"])\n",
+    "data.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0,0.5,'NL Beer consumption(x1000 hectoliter)')"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fec7fe376d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.title(\"Number of university students vs beer consumption from 2006-2018\")\n",
+    "plt.rcParams[\"figure.dpi\"] = 300\n",
+    "plt.plot(data[\"WO [x1000]\"], data[\"NL Beer consumption [x1000 hectoliter]\"])\n",
+    "plt.xlabel(\"Number of university students in NL(x1000)\")\n",
+    "plt.ylabel(\"NL Beer consumption(x1000 hectoliter)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/beer_consumption.png b/beer_consumption.png
new file mode 100644
index 0000000..9039002
Binary files /dev/null and b/beer_consumption.png differ
diff --git a/solution_.md b/solution_.md
new file mode 100644
index 0000000..6bec7ad
--- /dev/null
+++ b/solution_.md
@@ -0,0 +1,15 @@
+## Following are the titles of the papers which might be of our interest
+ * The Rise of Coccidioides: Forces Against the Dust Devil Unleashed(*MCC Van Dyke et al., 2019*)
+ * An analysis of the forces required to drag sheep over various surfaces(*JT Harvey, Applied Ergonomics, 2002*)
+ * Correlation Of Continuous Cardiac Output Measured By A Pulmonary Artery Catheter Versus Impedance Cardiography \
+ In Ventilated Patients(*DW Ziegler et al., 2005*)
+ 
+ 
+## Analysis of the Beer consumption data
+
+![Beer consumption vs number of university students in NL](beer_consumption.png)
+
+From the image above it looks like that the consumption of alcohol in the Netherlands increases as the number of university\
+going students increases. Unfortunately, this data is not sufficient to establish correlation between these factors. \
+But anyway, a hypothesis can be given. Students, as a group, tend to drink a lot more as compared to other groups in the society.\
+Another hypothesis could be that after getting a university degree, an individual can afford to buy more amount of beer.