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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# UFCFVQ-15-M Programming for Data Science\n",
+    "# Programming Task 2\n",
+    "\n",
+    "## Student Id: 23086369"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Requirement FR2.1 - Read CSV data from a file (with a header row) into memory "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Unnamed: 0  id_student gender                region      highest_education  \\\n",
+      "0           0       11391      M   East Anglian Region       HE Qualification   \n",
+      "1           1       28400      F              Scotland       HE Qualification   \n",
+      "2           2       31604      F     South East Region  A Level or Equivalent   \n",
+      "3           3       32885      F  West Midlands Region     Lower Than A Level   \n",
+      "4           4       38053      M                 Wales  A Level or Equivalent   \n",
+      "\n",
+      "  age_band disability final_result  score  \n",
+      "0     55<=          N         Pass   82.0  \n",
+      "1    35-55          N         Pass   67.0  \n",
+      "2    35-55          N         Pass   76.0  \n",
+      "3     0-35          N         Pass   55.0  \n",
+      "4    35-55          N         Pass   68.0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Importing the pandas library\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Read data, from a CSV file. Store it in a DataFrame.\n",
+    "\n",
+    "df = pd.read_csv(\"/Users/mscdatascience/Documents/assignment-PDS/mohammad_alsuulaimani_uwe_23086369_2023/task2a.csv\")\n",
+    "\n",
+    "# Display the five rows of the DataFrame to quickly examine its structure and content.\n",
+    "\n",
+    "print(df.head())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### MARK:      \n",
+    "#### FEEDBACK:    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Requirement FR2.2 - Read CSV data from a file (without a header row) into memory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   id_student  click_events\n",
+      "0        6516        2791.0\n",
+      "1        8462         656.0\n",
+      "2       11391         934.0\n",
+      "3       23629           NaN\n",
+      "4       23698         910.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Importing the pandas library\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Read data, from a CSV file.\n",
+    "# The columns are labeled as 'id_student' and 'click_events.\n",
+    "\n",
+    "df = pd.read_csv(\"/Users/mscdatascience/Documents/assignment-PDS/mohammad_alsuulaimani_uwe_23086369_2023/task2b.csv\", names=['id_student', 'click_events'])\n",
+    "\n",
+    "# Display the five rows of the DataFrame to quickly examine its structure and content.\n",
+    "\n",
+    "print(df.head())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### MARK:      \n",
+    "#### FEEDBACK:    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Requirement FR2.3 - Merge the data from two Dataframes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Unnamed: 0  id_student gender                region      highest_education  \\\n",
+      "0           0       11391      M   East Anglian Region       HE Qualification   \n",
+      "1           1       28400      F              Scotland       HE Qualification   \n",
+      "2           2       31604      F     South East Region  A Level or Equivalent   \n",
+      "3           3       32885      F  West Midlands Region     Lower Than A Level   \n",
+      "4           4       38053      M                 Wales  A Level or Equivalent   \n",
+      "\n",
+      "  age_band disability final_result  score  click_events  \n",
+      "0     55<=          N         Pass   82.0         934.0  \n",
+      "1    35-55          N         Pass   67.0        1435.0  \n",
+      "2    35-55          N         Pass   76.0        2158.0  \n",
+      "3     0-35          N         Pass   55.0        1034.0  \n",
+      "4    35-55          N         Pass   68.0        2445.0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Importing the pandas library\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Read data, from a CSV file in a DataFrame1 & DataFrame2.\n",
+    "\n",
+    "\n",
+    "Dataframe1 = pd.read_csv('task2a.csv')\n",
+    "Dataframe2 = pd.read_csv('task2b.csv', names=['id_student', 'click_events'])\n",
+    "\n",
+    "# Merging DataFrame1 & DataFrame2 into a new DataFrame.\n",
+    "# How ? By utilizing the 'inner' merge technique we combine the rows, in both DataFrames that share common 'id_student' values.\n",
+    "\n",
+    "merged_data_frame = pd.merge(Dataframe1, Dataframe2, on='id_student', how='inner')\n",
+    "\n",
+    "# Display the five rows of the mergd DataFrame.\n",
+    "\n",
+    "print(merged_data_frame.head())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### MARK:      \n",
+    "#### FEEDBACK:    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Requirement FR2.4 - Remove any rows that contain missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Unnamed: 0  id_student gender                region      highest_education  \\\n",
+      "0           0       11391      M   East Anglian Region       HE Qualification   \n",
+      "1           1       28400      F              Scotland       HE Qualification   \n",
+      "2           2       31604      F     South East Region  A Level or Equivalent   \n",
+      "3           3       32885      F  West Midlands Region     Lower Than A Level   \n",
+      "4           4       38053      M                 Wales  A Level or Equivalent   \n",
+      "\n",
+      "  age_band disability final_result  score  click_events  \n",
+      "0     55<=          N         Pass   82.0         934.0  \n",
+      "1    35-55          N         Pass   67.0        1435.0  \n",
+      "2    35-55          N         Pass   76.0        2158.0  \n",
+      "3     0-35          N         Pass   55.0        1034.0  \n",
+      "4    35-55          N         Pass   68.0        2445.0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Removing rows containing missing values\n",
+    "cleaned_data_frame = merged_data_frame.dropna()\n",
+    "\n",
+    "# Displaying the cleaned new DataFrame\n",
+    "print(cleaned_data_frame.head())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### MARK:      \n",
+    "#### FEEDBACK:    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Requirement FR2.5 - Filter out unnecessary rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Unnamed: 0  id_student gender                region      highest_education  \\\n",
+      "0           0       11391      M   East Anglian Region       HE Qualification   \n",
+      "1           1       28400      F              Scotland       HE Qualification   \n",
+      "2           2       31604      F     South East Region  A Level or Equivalent   \n",
+      "3           3       32885      F  West Midlands Region     Lower Than A Level   \n",
+      "4           4       38053      M                 Wales  A Level or Equivalent   \n",
+      "\n",
+      "  age_band disability final_result  score  click_events  \n",
+      "0     55<=          N         Pass   82.0         934.0  \n",
+      "1    35-55          N         Pass   67.0        1435.0  \n",
+      "2    35-55          N         Pass   76.0        2158.0  \n",
+      "3     0-35          N         Pass   55.0        1034.0  \n",
+      "4    35-55          N         Pass   68.0        2445.0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Filtering unnecessary rows where 'click_events' is smaller than 10\n",
+    "filtered_data_frame = cleaned_data_frame[cleaned_data_frame['click_events'] >= 10]\n",
+    "\n",
+    "# Displaying the filtered DataFrame\n",
+    "print(filtered_data_frame.head())\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### MARK:      \n",
+    "#### FEEDBACK:    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Requirement FR2.6 - Rename the score column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Unnamed: 0  id_student gender                region      highest_education  \\\n",
+      "0           0       11391      M   East Anglian Region       HE Qualification   \n",
+      "1           1       28400      F              Scotland       HE Qualification   \n",
+      "2           2       31604      F     South East Region  A Level or Equivalent   \n",
+      "3           3       32885      F  West Midlands Region     Lower Than A Level   \n",
+      "4           4       38053      M                 Wales  A Level or Equivalent   \n",
+      "\n",
+      "  age_band disability final_result  final_mark  click_events  \n",
+      "0     55<=          N         Pass        82.0         934.0  \n",
+      "1    35-55          N         Pass        67.0        1435.0  \n",
+      "2    35-55          N         Pass        76.0        2158.0  \n",
+      "3     0-35          N         Pass        55.0        1034.0  \n",
+      "4    35-55          N         Pass        68.0        2445.0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Renaming the 'score' column to 'final_mark'\n",
+    "renamed_data_frame = filtered_data_frame.rename(columns={'score': 'final_mark'})\n",
+    "\n",
+    "# Displaying the DataFrame with the renamed column\n",
+    "print(renamed_data_frame.head())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### MARK:     \n",
+    "#### FEEDBACK:    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Requirement FR2.7 - Remove unnecessary column(s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Unnamed: 0  id_student gender age_band disability  final_mark  click_events\n",
+      "0           0       11391      M     55<=          N        82.0         934.0\n",
+      "1           1       28400      F    35-55          N        67.0        1435.0\n",
+      "2           2       31604      F    35-55          N        76.0        2158.0\n",
+      "3           3       32885      F     0-35          N        55.0        1034.0\n",
+      "4           4       38053      M    35-55          N        68.0        2445.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Removing unnecessary rows from 'cleaned_data_frame' by using 'drop' method.\n",
+    "# The result will be stored in 'final_data_frame', which no longer includes 'region', 'final_result', 'highest_education' columns.\n",
+    "\n",
+    "final_data_frame = renamed_data_frame.drop(columns=['region', 'final_result', 'highest_education'])\n",
+    "\n",
+    "# Displaying the Final DataFrame\n",
+    "\n",
+    "print(final_data_frame.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### MARK:     \n",
+    "#### FEEDBACK:    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Requirement FR2.8 - Write the DataFrame data to a CSV file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Saving the 'final_data_frame' to a CSV file called 'updated.csv'.\n",
+    "# By using the 'index=False' parameter I can ensure that the CSV file does not include row indices.\n",
+    "\n",
+    "final_data_frame.to_csv('updated.csv', index=False)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### MARK:     \n",
+    "#### FEEDBACK:    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Requirement FR2.9 - Investigate the effects of age-group on attainment and engagement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          click_events  final_mark\n",
+      "age_band                          \n",
+      "0-35       1616.472655   72.503923\n",
+      "35-55      2193.000267   75.035810\n",
+      "55<=       3574.864865   77.718919\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculating the engagement and final mark for each age group.\n",
+    "average_by_age = final_data_frame.groupby('age_band').agg({'click_events': 'mean', 'final_mark': 'mean'})\n",
+    "\n",
+    "# The DataFrame will be displayed, showing the engagement and final mark, for each age group.\n",
+    "print(average_by_age)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### MARK:      \n",
+    "#### FEEDBACK:    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Requirement FR2.10 - Present the results of the age-group investigation using an appropriate visualisation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Creating the plot\n",
+    "fig, ax1 = plt.subplots()\n",
+    "\n",
+    "# Plotting the average final mark for each age band\n",
+    "ax1.bar(average_by_age.index, average_by_age['final_mark'], color='b', alpha=0.6, label='Final Mark')\n",
+    "\n",
+    "# Making the y-axis label and tick labels match the line color.\n",
+    "ax1.set_ylabel('Average Final Mark', color='b')\n",
+    "for tl in ax1.get_yticklabels():\n",
+    "    tl.set_color('b')\n",
+    "\n",
+    "# Setting x-axis label and title\n",
+    "ax1.set_xlabel('Age Band')\n",
+    "plt.title('Average Engagement and Final Mark by Age Band')\n",
+    "\n",
+    "# Creating another y-axis for the average click_events\n",
+    "ax2 = ax1.twinx()\n",
+    "ax2.plot(average_by_age.index, average_by_age['click_events'], color='r', label='Click Events')\n",
+    "ax2.set_ylabel('Average Click Events', color='r')\n",
+    "for tl in ax2.get_yticklabels():\n",
+    "    tl.set_color('r')\n",
+    "\n",
+    "# Showing the plot\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### MARK:     \n",
+    "#### FEEDBACK:    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Requirement FR2.11 - Investigate the effects of engagement on attainment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'seaborn'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m/Users/mscdatascience/Documents/assignment-PDS/mohammad_alsuulaimani_uwe_23086369_2023/UFCFVQ-15-M Programming Task 2 Template.ipynb Cell 33\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/mscdatascience/Documents/assignment-PDS/mohammad_alsuulaimani_uwe_23086369_2023/UFCFVQ-15-M%20Programming%20Task%202%20Template.ipynb#X44sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mseaborn\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39msns\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/mscdatascience/Documents/assignment-PDS/mohammad_alsuulaimani_uwe_23086369_2023/UFCFVQ-15-M%20Programming%20Task%202%20Template.ipynb#X44sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/mscdatascience/Documents/assignment-PDS/mohammad_alsuulaimani_uwe_23086369_2023/UFCFVQ-15-M%20Programming%20Task%202%20Template.ipynb#X44sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m \u001b[39m# Creating a scatter plot to investigate the correlation between engagement and final mark\u001b[39;00m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'seaborn'"
+     ]
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "# Creating a scatter plot to investigate the correlation between engagement and final mark\n",
+    "sns.scatterplot(x='click_events', y='final_mark', data=final_data_frame)\n",
+    "\n",
+    "# Adding title and labels\n",
+    "plt.xlabel('Click Events')\n",
+    "plt.ylabel('Final Mark ')\n",
+    "\n",
+    "# Showing the plot\n",
+    "plt.show()\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### MARK:     \n",
+    "#### FEEDBACK:    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adherence to good coding style"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### MARK:      \n",
+    "#### FEEDBACK:    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Process Development Report for Task 2\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Write here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### MARK:      \n",
+    "#### FEEDBACK:    "
+   ]
+  }
+ ],
+ "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": "3.11.5"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "9ef62a9e119055cb3f7e8378d4eaf3b008dbaf8b8298b9c44f87df8240f3e8bc"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/UFCFVQ-15-M Programming Task 2 Template.ipynb b/UFCFVQ-15-M Programming Task 2 Template.ipynb
index fa24b42bb65b3be868a4dad17ec925eaeb667997..588ced36d9465fc6b6275e93d54e415ff9be6208 100644
--- a/UFCFVQ-15-M Programming Task 2 Template.ipynb	
+++ b/UFCFVQ-15-M Programming Task 2 Template.ipynb	
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -121,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -231,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -280,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -328,7 +328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -372,7 +372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -399,7 +399,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -439,11 +439,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# add code here"
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Creating the plot\n",
+    "fig, ax1 = plt.subplots()\n",
+    "\n",
+    "# Plotting the grade for each age group\n",
+    "\n",
+    "ax1.bar(average_by_age.index, average_by_age['final_mark'], color='b', alpha=0.6, label='Final Mark')\n",
+    "\n",
+    "# Ensuring the y axis label and tick labels are consistent, with the line color.\n",
+    "\n",
+    "ax1.set_ylabel('Average Final Mark', color='b')\n",
+    "for tl in ax1.get_yticklabels():\n",
+    "    tl.set_color('b')\n",
+    "\n",
+    "# Setting x-axis label and title\n",
+    "ax1.set_xlabel('Age Band')\n",
+    "plt.title('Average Engagement and Final Mark by Age Band')\n",
+    "\n",
+    "# Creating another y axis to represent the average number of click events\n",
+    "\n",
+    "ax2 = ax1.twinx()\n",
+    "ax2.plot(average_by_age.index, average_by_age['click_events'], color='r', label='Click Events')\n",
+    "ax2.set_ylabel('Average Click Events', color='r')\n",
+    "for tl in ax2.get_yticklabels():\n",
+    "    tl.set_color('r')\n",
+    "\n",
+    "# Displaying the plot\n",
+    "plt.show()\n",
+    "\n",
+    "\n"
    ]
   },
   {
@@ -466,9 +507,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "# add code here"
-   ]
+   "source": []
   },
   {
    "cell_type": "markdown",