M10.02.13- Challenge (K-Means Clustering Solution).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6e8bf929-1459-4c01-aa2b-924e36c0ba63",
   "metadata": {},
   "source": [
    "# K-Means Clustering: Student Performance Analysis\n",
    "\n",
    "## **Scenario**\n",
    "Imagine you are a data scientist working for a school that wants to better understand student performance patterns. The goal is to segment students into distinct groups based on their academic achievements and engagement levels. By identifying these groups, the school aims to tailor support programs, personalise teaching strategies, and improve overall student success.\n",
    "\n",
    "## **Dataset Features**\n",
    "1. **Test Scores**: Average scores from assessments.\n",
    "2. **Assignment Completion Rate**: Percentage of assignments completed by each student.\n",
    "3. **Class Participation**: Number of participation points earned in class activities.\n",
    "4. **Study Hours per Week**: Average time spent studying outside of class.\n",
    "5. **Attendance Rate**: Percentage of classes attended.\n",
    "6. **Extra-Curricular Activities**: Number of extra-curricular activities participated in.\n",
    "\n",
    "## **Your Challenge Objective**\n",
    "Group students into clusters to identify patterns in their performance and engagement, such as:\n",
    "- High-performing, well-rounded students.\n",
    "- Average-performing students with room for improvement.\n",
    "- Low-performing students needing additional support.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e2adc41-8314-4ca2-a1de-949a7f3a9733",
   "metadata": {},
   "source": [
    "## Step 1: Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5f2d206e-601c-41d2-934d-fe0dd363f778",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "import pandas as pd  # For data manipulation and analysis\n",
    "import numpy as np  # For numerical computations\n",
    "from sklearn.cluster import KMeans  # For K-Means clustering algorithm\n",
    "from sklearn.preprocessing import StandardScaler  # For standardising features\n",
    "import matplotlib.pyplot as plt  # For creating visualisations\n",
    "import seaborn as sns  # For enhanced data visualisation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54881ce2-6a07-4fd0-84c7-b89de05fb1aa",
   "metadata": {},
   "source": [
    "## Step 2: Load the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6c36e895-2092-428b-b4e2-3bdfbfd41bc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Test_Scores  Assignment_Completion_Rate  Class_Participation  \\\n",
      "0           61                          67                    7   \n",
      "1           92                          92                   22   \n",
      "2           56                          69                    8   \n",
      "3           57                          61                    0   \n",
      "4           92                          95                   22   \n",
      "\n",
      "   Study_Hours_per_Week  Attendance_Rate  Extra_Curricular_Activities  \n",
      "0                     7               72                            0  \n",
      "1                    15               94                            4  \n",
      "2                     8               76                            0  \n",
      "3                     9               72                            1  \n",
      "4                    15               92                            3  \n"
     ]
    }
   ],
   "source": [
    "# Load the dataset\n",
    "file_path = 'Students_Performance.csv'\n",
    "data = pd.read_csv(file_path)\n",
    "\n",
    "# Display the first few rows\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "555c5df1-3753-4ac3-9dca-5387a6ee5053",
   "metadata": {},
   "source": [
    "## Step 3: Standardising the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9003ac55-a5fa-4ea2-b8cc-6f755be5d8ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialise the StandardScaler\n",
    "scaler = StandardScaler() # StandardScaler is a preprocessing tool that standardises the features in the dataset. It scales each feature to have a mean of 0 and a standard deviation of 1.\n",
    "scaled_data = scaler.fit_transform(data) # fit_transform() calculates the mean and standard deviation for scaling, and then transforms the data accordingly."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dedfa5c4-bea6-4a2f-a60d-22acb028c4d9",
   "metadata": {},
   "source": [
    "## Step 4: Use Elbow method to find optimal number of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "bce09836-ae34-49f3-84d5-99e1867dc5cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Admin\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Admin\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Admin\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Admin\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Admin\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Admin\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Admin\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Admin\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Admin\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "\n",
    "inertia = []\n",
    "# Loop through cluster counts from 1 to 10\n",
    "for k in range(1, 11):\n",
    "    # Initialise KMeans with k clusters\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    # Fit the KMeans model to the scaled data\n",
    "    kmeans.fit(scaled_data)\n",
    "    # Append the inertia value (sum of squared distances) to the list\n",
    "    inertia.append(kmeans.inertia_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ab82e9b-afb8-4953-b4df-19927a3235f8",
   "metadata": {},
   "source": [
    "## Step 5: Plotting the Elbow Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "09c79987-094c-45bd-82f1-643e50a7ca8c",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the number of clusters (x-axis) against the inertia values (y-axis)\n",
    "plt.plot(range(1, 11), inertia, marker='o')\n",
    "\n",
    "# Set the title of the plot to indicate the purpose of the visualisation\n",
    "plt.title('Elbow Method for Optimal Clusters')\n",
    "\n",
    "# Label the x-axis to indicate it represents the number of clusters\n",
    "plt.xlabel('Number of Clusters')\n",
    "\n",
    "# Label the y-axis to indicate it represents the inertia values\n",
    "plt.ylabel('Inertia')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7801a062-05ff-4405-a5ea-2f19a0b34aef",
   "metadata": {},
   "source": [
    "The Elbow Chart showed an \"elbow\" at **3 clusters**, indicating that this is the optimal number. Therefore, n_clusters=3.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "d1308601-71be-44f8-a287-aa3052afea4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1429: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# random_state=42 ensures reproducibility of results\n",
    "kmeans = KMeans(n_clusters=3, random_state=42)\n",
    "\n",
    "# Fit the KMeans model to the scaled data to compute the cluster assignments\n",
    "kmeans.fit(scaled_data)\n",
    "\n",
    "# Add the cluster labels assigned to each data point as a new column in the dataset\n",
    "data['Cluster'] = kmeans.labels_\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "132acd2a-6097-4ec9-8be2-6bef82b8cefd",
   "metadata": {},
   "source": [
    "### Step 6: Visualising Clusters Using Principal Component Analysis (PCA) for Dimensionality Reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "7b28f050-59b7-447a-aa15-895ba1252e29",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# Reducing dimensions for visualisation\n",
    "pca = PCA(n_components=2)\n",
    "pca_data = pca.fit_transform(scaled_data)\n",
    "\n",
    "# Adding cluster labels\n",
    "data['PCA1'] = pca_data[:, 0]\n",
    "data['PCA2'] = pca_data[:, 1]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1780c4f2-fde3-42d2-acbc-7465c275fa47",
   "metadata": {},
   "source": [
    "## Step 7: Plotting the Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "a99a1e2b-958f-4290-b0a6-8fbd50559849",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the figure size for the plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "sns.scatterplot(x='PCA1', y='PCA2', hue='Cluster', data=data, palette='viridis')\n",
    "# Create a scatter plot of the clusters using PCA components for visualisation\n",
    "# 'x' and 'y' correspond to the first two principal components (PCA1 and PCA2)\n",
    "# 'hue' differentiates the clusters using the 'Cluster' column in the dataset\n",
    "# 'data' is the dataset containing the PCA-transformed features and cluster labels\n",
    "# 'palette' sets the color scheme for the clusters\n",
    "\n",
    "\n",
    "\n",
    "# Add a title to the plot\n",
    "plt.title('Customer Segmentation Clusters')\n",
    "\n",
    "# Label the x-axis as 'PCA1'\n",
    "plt.xlabel('PCA1')\n",
    "\n",
    "# Label the y-axis as 'PCA2'\n",
    "plt.ylabel('PCA2')\n",
    "\n",
    "# Add a legend with the title 'Cluster' to indicate which color represents each cluster\n",
    "plt.legend(title='Cluster')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99eadb63-865e-4485-b231-c50c98b777ff",
   "metadata": {},
   "source": [
    "## Step 8: Analysing Cluster Characteristics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "38b91b8a-a05d-4cc8-9256-4543633f59b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Test_Scores  Assignment_Completion_Rate  Class_Participation  \\\n",
      "Cluster                                                                 \n",
      "0          58.225806                   64.935484             4.612903   \n",
      "1          91.666667                   93.666667            23.606061   \n",
      "2          75.333333                   80.694444            15.055556   \n",
      "\n",
      "         Study_Hours_per_Week  Attendance_Rate  Extra_Curricular_Activities  \\\n",
      "Cluster                                                                       \n",
      "0                    6.870968        74.129032                     0.580645   \n",
      "1                   16.484848        94.606061                     3.636364   \n",
      "2                   11.944444        84.305556                     1.472222   \n",
      "\n",
      "             PCA1      PCA2  \n",
      "Cluster                      \n",
      "0       -2.883099  0.178922  \n",
      "1        2.809869  0.176176  \n",
      "2       -0.093045 -0.315566  \n"
     ]
    }
   ],
   "source": [
    "cluster_analysis = data.groupby('Cluster').mean()\n",
    "print(cluster_analysis)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68a806df-820f-41ec-97f1-22d2b3c14f36",
   "metadata": {},
   "source": [
    "### **Interpretation and Summary of Cluster Profiles**\n",
    "\n",
    "The clustering results reveal three distinct student performance groups based on their academic engagement and achievements. Here's the interpretation for each cluster:\n",
    "\n",
    "### **Cluster 0: Low-Performing Students**\n",
    "- **Test Scores**: Average of ~58.23, indicating below-average performance in tests.\n",
    "- **Assignment Completion Rate**: ~64.93%, showing inconsistent assignment submissions.\n",
    "- **Class Participation**: ~4.61, reflecting minimal engagement in class activities.\n",
    "- **Study Hours per Week**: ~6.87 hours, suggesting limited study efforts outside of class.\n",
    "- **Attendance Rate**: ~74.13%, indicating frequent absences from class.\n",
    "- **Extra-Curricular Activities**: ~0.58, showing little to no participation in extra-curricular activities.\n",
    "\n",
    "**Insights**:\n",
    "- Cluster 0 consists of students struggling academically and showing low engagement.\n",
    "- They may benefit from additional support, such as personalized tutoring or mentoring programs.\n",
    "\n",
    "### **Cluster 1: High-Performing Students**\n",
    "- **Test Scores**: Average of ~91.67, indicating excellent academic performance.\n",
    "- **Assignment Completion Rate**: ~93.67%, reflecting high consistency in submitting assignments.\n",
    "- **Class Participation**: ~23.61, showing active involvement in class discussions and activities.\n",
    "- **Study Hours per Week**: ~16.48 hours, demonstrating significant effort in studying outside of class.\n",
    "- **Attendance Rate**: ~94.61%, indicating near-perfect attendance.\n",
    "- **Extra-Curricular Activities**: ~3.64, showing strong involvement in extra-curricular activities.\n",
    "\n",
    "**Insights**:\n",
    "- Cluster 1 represents high-achieving, well-rounded students excelling both academically and socially.\n",
    "- They could be encouraged to take on leadership roles or participate in advanced academic projects.\n",
    "\n",
    "### **Cluster 2: Average-Performing Students**\n",
    "- **Test Scores**: Average of ~75.33, indicating moderate academic performance.\n",
    "- **Assignment Completion Rate**: ~80.69%, reflecting reasonably consistent submission of assignments.\n",
    "- **Class Participation**: ~15.06, showing moderate engagement in class activities.\n",
    "- **Study Hours per Week**: ~11.94 hours, reflecting average study efforts.\n",
    "- **Attendance Rate**: ~84.31%, indicating reasonably good attendance.\n",
    "- **Extra-Curricular Activities**: ~1.47, showing limited involvement in extra-curricular activities.\n",
    "\n",
    "**Insights**:\n",
    "- Cluster 2 consists of students performing at an average level, with room for improvement in both academics and engagement.\n",
    "- They may benefit from additional motivation, such as targeted rewards or structured study plans.\n",
    "\n",
    "### **Overall Summary**\n",
    "- **Cluster 0** includes students who are academically at risk and need focused support.\n",
    "- **Cluster 1** represents high-performing students who excel in both academics and extra-curricular activities.\n",
    "- **Cluster 2** consists of average-performing students with potential for growth through targeted interventions.\n",
    "\n",
    "These insights can guide educators in designing tailored strategies to address the specific needs of each group, fostering better learning outcomes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "855c3c37-804e-4690-836a-531f75fb80b8",
   "metadata": {},
   "source": [
    "## What's Next? "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0fbbb21-46ed-4533-8335-a4f0832b7071",
   "metadata": {},
   "source": [
    "Up next, we will have a Lesson Wrap-Up, where we will summarise key concepts covered so far and highlight the main takeaways. Stay tuned!"
   ]
  }
 ],
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