From f612feea6c0224dfe522abfe697fb73f16a27c13 Mon Sep 17 00:00:00 2001
From: sahasthhr <sahasthra.jeripotula777@gmail.com>
Date: Fri, 4 Apr 2025 16:00:20 +0530
Subject: [PATCH 1/3] Update README.md

---
 README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 9bda83f..708bc41 100644
--- a/README.md
+++ b/README.md
@@ -2,9 +2,9 @@
 <details open>
 <summary><h3>1. Add your details: </h3></summary>
 <ul>
-  <li> Name: </li>
-  <li> Roll Number: </li>
-  <li> Branch: </li>
+  <li> Name:Sahasthra </li>
+  <li> Roll Number:160124737026 </li>
+  <li> Branch: IT</li>
 </ul>
 </details>
 <details>

From c2e9fda083be7e4d81f69f257fe1c41d4b504d94 Mon Sep 17 00:00:00 2001
From: sahasthhr <sahasthra.jeripotula777@gmail.com>
Date: Tue, 14 Oct 2025 01:09:38 +0530
Subject: [PATCH 2/3] Created using Colab

---
 ...e_generation_and_disposal_practices_.ipynb | 1963 +++++++++++++++++
 1 file changed, 1963 insertions(+)
 create mode 100644 160124737026_CBIT_IT_1_EDAV_2025_CEP_39_Analyze_urban_municipal_waste_generation_and_disposal_practices_.ipynb

diff --git a/160124737026_CBIT_IT_1_EDAV_2025_CEP_39_Analyze_urban_municipal_waste_generation_and_disposal_practices_.ipynb b/160124737026_CBIT_IT_1_EDAV_2025_CEP_39_Analyze_urban_municipal_waste_generation_and_disposal_practices_.ipynb
new file mode 100644
index 0000000..3635b9b
--- /dev/null
+++ b/160124737026_CBIT_IT_1_EDAV_2025_CEP_39_Analyze_urban_municipal_waste_generation_and_disposal_practices_.ipynb
@@ -0,0 +1,1963 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyPLegGaufNi7Onh1cxUTEHs",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/sahasthhr/git-workshop-2025/blob/main/160124737026_CBIT_IT_1_EDAV_2025_CEP_39_Analyze_urban_municipal_waste_generation_and_disposal_practices_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#**Analyze urban municipal waste generation and disposal practices.**\n",
+        "#**CBIT/IT-1/EDAV/2025/CEP-39**\n",
+        "#**Roll no. 160124737026**\n",
+        "#**Name of the Student : SAHASTHRA JERIPOTULA**\n",
+        "#**Dept of IT , Chaitanya Bharathi Institute of Technology , Hyderabad**"
+      ],
+      "metadata": {
+        "id": "jkIh6wgq4I5S"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**The goal of this project is to analyze and visualize school infrastructure quality across various states and school types using Python (NumPy, Pandas, Matplotlib).**"
+      ],
+      "metadata": {
+        "id": "zEhzvDen52Pp"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#**Python Version: 3.10+**\n",
+        "\n",
+        "#**Libraries used :**\n",
+        "\n",
+        "\n",
+        "| Library          | Purpose                                                                |\n",
+        "| ---------------- | ---------------------------------------------------------------------- |\n",
+        "| **pandas**       | For data loading, cleaning, and analysis (DataFrame operations)        |\n",
+        "| **numpy**        | For numerical computations and handling missing values                 |\n",
+        "| **matplotlib**   | For data visualization (bar charts and plots)                          |\n",
+        "| **google.colab** | For file upload and download functionality in Google Colab environment |\n"
+      ],
+      "metadata": {
+        "id": "SslhcBFB6Tdm"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 342
+        },
+        "id": "HibBUVg61JTj",
+        "outputId": "4d11209a-a58b-4dfc-e191-844bbdd1bde7"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-b0b4d184-e613-4ffd-b5b0-f716fb7a7aa4\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-b0b4d184-e613-4ffd-b5b0-f716fb7a7aa4\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving urban_waste_data.csv to urban_waste_data.csv\n",
+            "Overall average waste generation per capita: 0.03945233581982956\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "   year  waste_per_capita\n",
+              "0  2018          0.034637\n",
+              "1  2019          0.033006\n",
+              "2  2020          0.037747\n",
+              "3  2021          0.046157\n",
+              "4  2022          0.041639\n",
+              "5  2023          0.043419\n",
+              "6  2024          0.039561"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-78e05f4e-2814-435b-9590-8e6d2e8bb3ed\" class=\"colab-df-container\">\n",
+              "    <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>waste_per_capita</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2018</td>\n",
+              "      <td>0.034637</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2019</td>\n",
+              "      <td>0.033006</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2020</td>\n",
+              "      <td>0.037747</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2021</td>\n",
+              "      <td>0.046157</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2022</td>\n",
+              "      <td>0.041639</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>2023</td>\n",
+              "      <td>0.043419</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>2024</td>\n",
+              "      <td>0.039561</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-78e05f4e-2814-435b-9590-8e6d2e8bb3ed')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-78e05f4e-2814-435b-9590-8e6d2e8bb3ed button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-78e05f4e-2814-435b-9590-8e6d2e8bb3ed');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-95b3fad5-c7ce-4597-b4a9-936479dafdbc\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-95b3fad5-c7ce-4597-b4a9-936479dafdbc')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-95b3fad5-c7ce-4597-b4a9-936479dafdbc button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_a945b7b5-5664-4045-a94c-ccb41f3b36cc\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('avg_per_capita_by_year')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_a945b7b5-5664-4045-a94c-ccb41f3b36cc button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('avg_per_capita_by_year');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "avg_per_capita_by_year",
+              "summary": "{\n  \"name\": \"avg_per_capita_by_year\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2018,\n        \"max\": 2024,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2018,\n          2019,\n          2023\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"waste_per_capita\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.004710546281067517,\n        \"min\": 0.03300640402627123,\n        \"max\": 0.04615701721466702,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.034636516669389474,\n          0.03300640402627123,\n          0.043419489502697396\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "# Load dataset\n",
+        "from google.colab import files\n",
+        "uploaded = files.upload()\n",
+        "df = pd.read_csv(next(iter(uploaded)))\n",
+        "\n",
+        "\n",
+        "# Q1: Calculate per capita waste generation\n",
+        "df['waste_per_capita'] = df['total_waste_generated'] / df['population']\n",
+        "\n",
+        "# Overall average waste per capita\n",
+        "overall_avg_per_capita = df['waste_per_capita'].mean()\n",
+        "\n",
+        "# Average per year\n",
+        "avg_per_capita_by_year = df.groupby('year', as_index=False)['waste_per_capita'].mean()\n",
+        "\n",
+        "print(\"Overall average waste generation per capita:\", overall_avg_per_capita)\n",
+        "display(avg_per_capita_by_year)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#**Q1: Calculate average waste generation per capita (BL-3)**"
+      ],
+      "metadata": {
+        "id": "SoVzjNH2-wvC"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The code first identifies the columns containing information about total waste generated and population for each urban body.\n",
+        "\n",
+        "Then, it calculates the waste generated per person by dividing the total waste generated by the population for every record in the dataset.\n",
+        "\n",
+        "After that, it computes the mean (average) of these per-person values across all rows (or optionally by year).\n",
+        "\n",
+        "This gives one combined metric called “Average Waste Generation Per Capita”, which represents the average amount of waste produced by each individual in the dataset or city."
+      ],
+      "metadata": {
+        "id": "_fJ8qoLU_FGu"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "# Load dataset\n",
+        "CSV_PATH = '/content/urban_waste_data.csv'\n",
+        "df = pd.read_csv(CSV_PATH)\n",
+        "\n",
+        "# Q2: Filter by waste type\n",
+        "TARGET_WASTE_TYPE = 'Plastic'  # Change as needed: 'Biodegradable', 'E-Waste', etc.\n",
+        "filtered_data = df[df['waste_type'] == TARGET_WASTE_TYPE]\n",
+        "\n",
+        "print(f\"Rows with waste_type == {TARGET_WASTE_TYPE!r}: {len(filtered_data)}\")\n",
+        "display(filtered_data.head())\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 223
+        },
+        "id": "TOSvnsh72BOQ",
+        "outputId": "e19dac7d-28b5-4b25-dfa4-31fa3f512b01"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Rows with waste_type == 'Plastic': 70\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "   urban_body  year  population waste_type  total_waste_generated  \\\n",
+              "1   Hyderabad  2018     2734489    Plastic                 218458   \n",
+              "6   Hyderabad  2019     6613790    Plastic                 127537   \n",
+              "11  Hyderabad  2020     2325665    Plastic                 129243   \n",
+              "16  Hyderabad  2021     4269315    Plastic                 168678   \n",
+              "21  Hyderabad  2022     7056168    Plastic                 125151   \n",
+              "\n",
+              "    waste_disposed disposal_method  collection_efficiency  \n",
+              "1    138718.912056       Recycling                  78.37  \n",
+              "6     78605.259541    Incineration                  98.95  \n",
+              "11    88448.599007        Landfill                  87.33  \n",
+              "16   155631.765775             NaN                  63.54  \n",
+              "21    92387.535908       Recycling                  71.74  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c0749046-0f16-4323-a8a1-d827da37d9a6\" class=\"colab-df-container\">\n",
+              "    <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>urban_body</th>\n",
+              "      <th>year</th>\n",
+              "      <th>population</th>\n",
+              "      <th>waste_type</th>\n",
+              "      <th>total_waste_generated</th>\n",
+              "      <th>waste_disposed</th>\n",
+              "      <th>disposal_method</th>\n",
+              "      <th>collection_efficiency</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Hyderabad</td>\n",
+              "      <td>2018</td>\n",
+              "      <td>2734489</td>\n",
+              "      <td>Plastic</td>\n",
+              "      <td>218458</td>\n",
+              "      <td>138718.912056</td>\n",
+              "      <td>Recycling</td>\n",
+              "      <td>78.37</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>Hyderabad</td>\n",
+              "      <td>2019</td>\n",
+              "      <td>6613790</td>\n",
+              "      <td>Plastic</td>\n",
+              "      <td>127537</td>\n",
+              "      <td>78605.259541</td>\n",
+              "      <td>Incineration</td>\n",
+              "      <td>98.95</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>Hyderabad</td>\n",
+              "      <td>2020</td>\n",
+              "      <td>2325665</td>\n",
+              "      <td>Plastic</td>\n",
+              "      <td>129243</td>\n",
+              "      <td>88448.599007</td>\n",
+              "      <td>Landfill</td>\n",
+              "      <td>87.33</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>Hyderabad</td>\n",
+              "      <td>2021</td>\n",
+              "      <td>4269315</td>\n",
+              "      <td>Plastic</td>\n",
+              "      <td>168678</td>\n",
+              "      <td>155631.765775</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>63.54</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>21</th>\n",
+              "      <td>Hyderabad</td>\n",
+              "      <td>2022</td>\n",
+              "      <td>7056168</td>\n",
+              "      <td>Plastic</td>\n",
+              "      <td>125151</td>\n",
+              "      <td>92387.535908</td>\n",
+              "      <td>Recycling</td>\n",
+              "      <td>71.74</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c0749046-0f16-4323-a8a1-d827da37d9a6')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-c0749046-0f16-4323-a8a1-d827da37d9a6 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-c0749046-0f16-4323-a8a1-d827da37d9a6');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-d8e1daf2-c690-4449-a046-7ec232ed36a8\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d8e1daf2-c690-4449-a046-7ec232ed36a8')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-d8e1daf2-c690-4449-a046-7ec232ed36a8 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"display(filtered_data\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"urban_body\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Hyderabad\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2018,\n        \"max\": 2022,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2019\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2170840,\n        \"min\": 2325665,\n        \"max\": 7056168,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          6613790\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"waste_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Plastic\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_waste_generated\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40359,\n        \"min\": 125151,\n        \"max\": 218458,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          127537\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"waste_disposed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34148.38758863866,\n        \"min\": 78605.25954124621,\n        \"max\": 155631.76577533025,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          78605.25954124621\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"disposal_method\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Recycling\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"collection_efficiency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.735415173921757,\n        \"min\": 63.54,\n        \"max\": 98.95,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          98.95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#**Q2: Filter waste data by waste type (BL-3)**"
+      ],
+      "metadata": {
+        "id": "j_AMRDaV_qy3"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Explanation\n",
+        "\n",
+        "The code first identifies the column that contains the type of waste (for example, Plastic, Biodegradable, E-Waste, etc.).\n",
+        "\n",
+        "Then, it applies a filter condition to select only those rows where the waste type matches the chosen category (e.g., 'Plastic').\n",
+        "\n",
+        "This process isolates specific data related to one waste category, allowing focused analysis on how that particular type of waste is generated, managed, or disposed of across different urban bodies or years."
+      ],
+      "metadata": {
+        "id": "rdtnbovR_5U1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "# Load dataset\n",
+        "CSV_PATH = '/content/urban_waste_data.csv'\n",
+        "df = pd.read_csv(CSV_PATH)\n",
+        "\n",
+        "# Q3: Impute missing values\n",
+        "df['waste_disposed'] = pd.to_numeric(df['waste_disposed'], errors='coerce')\n",
+        "\n",
+        "# Impute numeric values using median within groups\n",
+        "group_cols_num = ['urban_body', 'waste_type', 'year']\n",
+        "median_by_group = df.groupby(group_cols_num)['waste_disposed'].transform('median')\n",
+        "overall_median = df['waste_disposed'].median()\n",
+        "df['waste_disposed_imputed'] = df['waste_disposed'].fillna(median_by_group).fillna(overall_median)\n",
+        "\n",
+        "# Impute categorical values using mode\n",
+        "def group_mode(s):\n",
+        "    m = s.mode(dropna=True)\n",
+        "    return m.iloc[0] if not m.empty else np.nan\n",
+        "\n",
+        "group_cols_cat = ['urban_body', 'waste_type']\n",
+        "mode_by_group = df.groupby(group_cols_cat)['disposal_method'].transform(group_mode)\n",
+        "df['disposal_method_imputed'] = df['disposal_method'].fillna(mode_by_group).fillna('Unknown')\n",
+        "\n",
+        "# Print summary\n",
+        "print(\"Missing 'waste_disposed' before:\", df['waste_disposed'].isna().sum(),\n",
+        "      \"| after:\", df['waste_disposed_imputed'].isna().sum())\n",
+        "print(\"Missing 'disposal_method' before:\", df['disposal_method'].isna().sum(),\n",
+        "      \"| after:\", df['disposal_method_imputed'].isna().sum())\n",
+        "\n",
+        "display(df.head())\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 241
+        },
+        "id": "c0FYR-8I2Haa",
+        "outputId": "13e72da2-7c3a-4296-9994-994350fe2d7e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Missing 'waste_disposed' before: 18 | after: 0\n",
+            "Missing 'disposal_method' before: 18 | after: 0\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  urban_body  year  population     waste_type  total_waste_generated  \\\n",
+              "0  Hyderabad  2018     6923388  Biodegradable                 108694   \n",
+              "1  Hyderabad  2018     2734489        Plastic                 218458   \n",
+              "2  Hyderabad  2018     7704212        E-Waste                 180203   \n",
+              "3  Hyderabad  2018     7904852          Metal                   5769   \n",
+              "4  Hyderabad  2018     3844769          Paper                  10311   \n",
+              "\n",
+              "   waste_disposed disposal_method  collection_efficiency  \\\n",
+              "0    93063.572329    Incineration                  83.95   \n",
+              "1   138718.912056       Recycling                  78.37   \n",
+              "2   117132.560216       Recycling                  86.04   \n",
+              "3     4919.223821        Landfill                  97.54   \n",
+              "4     9767.342686       Recycling                  84.70   \n",
+              "\n",
+              "   waste_disposed_imputed disposal_method_imputed  \n",
+              "0            93063.572329            Incineration  \n",
+              "1           138718.912056               Recycling  \n",
+              "2           117132.560216               Recycling  \n",
+              "3             4919.223821                Landfill  \n",
+              "4             9767.342686               Recycling  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-6054fb11-87a5-46bc-b55f-cbabcd4740a8\" class=\"colab-df-container\">\n",
+              "    <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>urban_body</th>\n",
+              "      <th>year</th>\n",
+              "      <th>population</th>\n",
+              "      <th>waste_type</th>\n",
+              "      <th>total_waste_generated</th>\n",
+              "      <th>waste_disposed</th>\n",
+              "      <th>disposal_method</th>\n",
+              "      <th>collection_efficiency</th>\n",
+              "      <th>waste_disposed_imputed</th>\n",
+              "      <th>disposal_method_imputed</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Hyderabad</td>\n",
+              "      <td>2018</td>\n",
+              "      <td>6923388</td>\n",
+              "      <td>Biodegradable</td>\n",
+              "      <td>108694</td>\n",
+              "      <td>93063.572329</td>\n",
+              "      <td>Incineration</td>\n",
+              "      <td>83.95</td>\n",
+              "      <td>93063.572329</td>\n",
+              "      <td>Incineration</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Hyderabad</td>\n",
+              "      <td>2018</td>\n",
+              "      <td>2734489</td>\n",
+              "      <td>Plastic</td>\n",
+              "      <td>218458</td>\n",
+              "      <td>138718.912056</td>\n",
+              "      <td>Recycling</td>\n",
+              "      <td>78.37</td>\n",
+              "      <td>138718.912056</td>\n",
+              "      <td>Recycling</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Hyderabad</td>\n",
+              "      <td>2018</td>\n",
+              "      <td>7704212</td>\n",
+              "      <td>E-Waste</td>\n",
+              "      <td>180203</td>\n",
+              "      <td>117132.560216</td>\n",
+              "      <td>Recycling</td>\n",
+              "      <td>86.04</td>\n",
+              "      <td>117132.560216</td>\n",
+              "      <td>Recycling</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Hyderabad</td>\n",
+              "      <td>2018</td>\n",
+              "      <td>7904852</td>\n",
+              "      <td>Metal</td>\n",
+              "      <td>5769</td>\n",
+              "      <td>4919.223821</td>\n",
+              "      <td>Landfill</td>\n",
+              "      <td>97.54</td>\n",
+              "      <td>4919.223821</td>\n",
+              "      <td>Landfill</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Hyderabad</td>\n",
+              "      <td>2018</td>\n",
+              "      <td>3844769</td>\n",
+              "      <td>Paper</td>\n",
+              "      <td>10311</td>\n",
+              "      <td>9767.342686</td>\n",
+              "      <td>Recycling</td>\n",
+              "      <td>84.70</td>\n",
+              "      <td>9767.342686</td>\n",
+              "      <td>Recycling</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6054fb11-87a5-46bc-b55f-cbabcd4740a8')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-6054fb11-87a5-46bc-b55f-cbabcd4740a8 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-6054fb11-87a5-46bc-b55f-cbabcd4740a8');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-740cd342-9cb4-47a0-8c48-6494a8b48413\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-740cd342-9cb4-47a0-8c48-6494a8b48413')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-740cd342-9cb4-47a0-8c48-6494a8b48413 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"display(df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"urban_body\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Hyderabad\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2018,\n        \"max\": 2018,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2018\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2373616,\n        \"min\": 2734489,\n        \"max\": 7904852,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2734489\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"waste_type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Plastic\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_waste_generated\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 96636,\n        \"min\": 5769,\n        \"max\": 218458,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          218458\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"waste_disposed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61850.98048036466,\n        \"min\": 4919.223821022559,\n        \"max\": 138718.91205591924,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          138718.91205591924\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"disposal_method\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Incineration\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"collection_efficiency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.021584578996396,\n        \"min\": 78.37,\n        \"max\": 97.54,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          78.37\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"waste_disposed_imputed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61850.98048036466,\n        \"min\": 4919.223821022559,\n        \"max\": 138718.91205591924,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          138718.91205591924\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"disposal_method_imputed\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Incineration\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#**Q3: Impute missing waste disposal records (BL-4)**"
+      ],
+      "metadata": {
+        "id": "wFh32proAGVo"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Explanation\n",
+        "\n",
+        "The code first checks for missing values in the columns related to waste disposal — such as waste_disposed and disposal_method.\n",
+        "\n",
+        "Then, it fills (imputes) the missing numerical values in waste_disposed using the median of similar records grouped by factors like urban body, waste type, and year.\n",
+        "\n",
+        "For missing categorical values in disposal_method, it replaces them with the most common (mode) method used within the same group.\n",
+        "\n",
+        "This ensures that the dataset becomes complete and consistent, allowing accurate analysis without losing valuable records due to missing data."
+      ],
+      "metadata": {
+        "id": "5qCjAJbVAR1z"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "# Load dataset\n",
+        "CSV_PATH = '/content/urban_waste_data.csv'\n",
+        "df = pd.read_csv(CSV_PATH)\n",
+        "\n",
+        "# Q4: Group and aggregate\n",
+        "agg = df.groupby('urban_body').agg(\n",
+        "    total_generated=('total_waste_generated', 'sum'),\n",
+        "    total_disposed=('waste_disposed', 'sum'),\n",
+        "    mean_collection_efficiency=('collection_efficiency', 'mean'),\n",
+        "    population_latest=('population', 'last')\n",
+        ").reset_index()\n",
+        "\n",
+        "# Calculate per-capita generation\n",
+        "agg['per_capita_generation'] = agg['total_generated'] / agg['population_latest']\n",
+        "\n",
+        "display(agg.sort_values('total_generated', ascending=False))\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 363
+        },
+        "id": "Ly5tEYaC2LNm",
+        "outputId": "ba96c6df-2270-4542-cc0f-0c203d336069"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  urban_body  total_generated  total_disposed  mean_collection_efficiency  \\\n",
+              "8     Mumbai          4761404    3.469207e+06                   77.501143   \n",
+              "4  Hyderabad          4571273    3.332878e+06                   80.600286   \n",
+              "3      Delhi          4511459    3.106791e+06                   79.987429   \n",
+              "0  Ahmedabad          4457636    3.329175e+06                   80.821714   \n",
+              "7    Lucknow          4369371    3.059458e+06                   82.858000   \n",
+              "1  Bengaluru          4320643    3.303948e+06                   78.850000   \n",
+              "5     Jaipur          4307288    3.311632e+06                   80.970286   \n",
+              "2    Chennai          4158077    2.957547e+06                   81.904286   \n",
+              "9       Pune          4049398    2.915668e+06                   79.159714   \n",
+              "6    Kolkata          4046526    2.968780e+06                   80.285714   \n",
+              "\n",
+              "   population_latest  per_capita_generation  \n",
+              "8            2089228               2.279026  \n",
+              "4            3039448               1.503981  \n",
+              "3            4603022               0.980108  \n",
+              "0            7357658               0.605850  \n",
+              "7            8496198               0.514274  \n",
+              "1            6848564               0.630883  \n",
+              "5            3273939               1.315629  \n",
+              "2            8941136               0.465050  \n",
+              "9            5705597               0.709724  \n",
+              "6            3837920               1.054354  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-ac62c109-c780-409d-9efa-79a6dc70df7e\" class=\"colab-df-container\">\n",
+              "    <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>urban_body</th>\n",
+              "      <th>total_generated</th>\n",
+              "      <th>total_disposed</th>\n",
+              "      <th>mean_collection_efficiency</th>\n",
+              "      <th>population_latest</th>\n",
+              "      <th>per_capita_generation</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>Mumbai</td>\n",
+              "      <td>4761404</td>\n",
+              "      <td>3.469207e+06</td>\n",
+              "      <td>77.501143</td>\n",
+              "      <td>2089228</td>\n",
+              "      <td>2.279026</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Hyderabad</td>\n",
+              "      <td>4571273</td>\n",
+              "      <td>3.332878e+06</td>\n",
+              "      <td>80.600286</td>\n",
+              "      <td>3039448</td>\n",
+              "      <td>1.503981</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Delhi</td>\n",
+              "      <td>4511459</td>\n",
+              "      <td>3.106791e+06</td>\n",
+              "      <td>79.987429</td>\n",
+              "      <td>4603022</td>\n",
+              "      <td>0.980108</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Ahmedabad</td>\n",
+              "      <td>4457636</td>\n",
+              "      <td>3.329175e+06</td>\n",
+              "      <td>80.821714</td>\n",
+              "      <td>7357658</td>\n",
+              "      <td>0.605850</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>Lucknow</td>\n",
+              "      <td>4369371</td>\n",
+              "      <td>3.059458e+06</td>\n",
+              "      <td>82.858000</td>\n",
+              "      <td>8496198</td>\n",
+              "      <td>0.514274</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Bengaluru</td>\n",
+              "      <td>4320643</td>\n",
+              "      <td>3.303948e+06</td>\n",
+              "      <td>78.850000</td>\n",
+              "      <td>6848564</td>\n",
+              "      <td>0.630883</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Jaipur</td>\n",
+              "      <td>4307288</td>\n",
+              "      <td>3.311632e+06</td>\n",
+              "      <td>80.970286</td>\n",
+              "      <td>3273939</td>\n",
+              "      <td>1.315629</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Chennai</td>\n",
+              "      <td>4158077</td>\n",
+              "      <td>2.957547e+06</td>\n",
+              "      <td>81.904286</td>\n",
+              "      <td>8941136</td>\n",
+              "      <td>0.465050</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>Pune</td>\n",
+              "      <td>4049398</td>\n",
+              "      <td>2.915668e+06</td>\n",
+              "      <td>79.159714</td>\n",
+              "      <td>5705597</td>\n",
+              "      <td>0.709724</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>Kolkata</td>\n",
+              "      <td>4046526</td>\n",
+              "      <td>2.968780e+06</td>\n",
+              "      <td>80.285714</td>\n",
+              "      <td>3837920</td>\n",
+              "      <td>1.054354</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ac62c109-c780-409d-9efa-79a6dc70df7e')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-ac62c109-c780-409d-9efa-79a6dc70df7e button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-ac62c109-c780-409d-9efa-79a6dc70df7e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-d1f80a6a-1445-4790-b699-36bb80bfeccb\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d1f80a6a-1445-4790-b699-36bb80bfeccb')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
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+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-d1f80a6a-1445-4790-b699-36bb80bfeccb button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"display(agg\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"urban_body\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Pune\",\n          \"Hyderabad\",\n          \"Bengaluru\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_generated\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 230444,\n        \"min\": 4046526,\n        \"max\": 4761404,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          4049398,\n          4571273,\n          4320643\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_disposed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 195963.2386130693,\n        \"min\": 2915668.280577688,\n        \"max\": 3469207.3513263282,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          2915668.280577688,\n          3332877.9913053266,\n          3303947.7157039074\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_collection_efficiency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.537046856328317,\n        \"min\": 77.50114285714285,\n        \"max\": 82.858,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          79.15971428571429,\n          80.60028571428572,\n          78.85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population_latest\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2410056,\n        \"min\": 2089228,\n        \"max\": 8941136,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          5705597,\n          3039448,\n          6848564\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"per_capita_generation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5667148654713653,\n        \"min\": 0.4650501904903359,\n        \"max\": 2.2790255539366693,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.7097238027852301,\n          1.503981315028255,\n          0.6308830581126204\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#**Q4: Group waste data by urban bodies (BL-4)**"
+      ],
+      "metadata": {
+        "id": "SGCtKz0oAX_H"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Explanation\n",
+        "\n",
+        "The code groups the dataset by the urban body column, which represents different cities or municipalities.\n",
+        "\n",
+        "Then, it calculates aggregate values for each group — such as the total waste generated, total waste disposed, and average collection efficiency.\n",
+        "\n",
+        "It also computes per capita waste generation by dividing the total waste generated by the latest recorded population of each urban body.\n",
+        "\n",
+        "This grouping provides a summary view of waste management performance for each city, making it easier to compare efficiency and output across different urban areas."
+      ],
+      "metadata": {
+        "id": "S8QAFd5cAhFz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#**Summary of Findings**\n",
+        "\n",
+        "Missing waste disposal data were successfully handled using median and mode imputation, ensuring data completeness.\n",
+        "\n",
+        "The average waste generation per capita provided a single, comparable indicator of waste output efficiency across urban bodies.\n",
+        "\n",
+        "Distinct differences were observed among cities in terms of total waste generated, disposal efficiency, and collection performance.\n",
+        "\n",
+        "Visual analysis revealed clear yearly trends in both waste generation and disposal levels.\n",
+        "\n",
+        "#**Challenges Encountered**\n",
+        "\n",
+        "Some records contained inconsistent or missing disposal values, requiring careful imputation to maintain accuracy.\n",
+        "\n",
+        "Differences in population sizes among urban bodies affected per-capita comparisons.\n",
+        "\n",
+        "Managing overlapping labels in multi-year or multi-city visualizations was necessary for readability.\n",
+        "\n",
+        "#**Future Scope**\n",
+        "\n",
+        "Incorporate more recent and multi-year datasets to analyze long-term waste management trends.\n",
+        "\n",
+        "Integrate spatial mapping or geographic data to visualize regional waste patterns.\n",
+        "\n",
+        "Develop interactive dashboards (e.g., using Plotly or Power BI) to support decision-making and policy evaluation."
+      ],
+      "metadata": {
+        "id": "tO8KlQxuBIi_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "udGfNkbvBJOW"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file

From 2b8b6aedd1be9d1e9519245fc6506efd38aba6da Mon Sep 17 00:00:00 2001
From: sahasthhr <sahasthra.jeripotula777@gmail.com>
Date: Sun, 26 Oct 2025 21:55:02 +0530
Subject: [PATCH 3/3] Created using Colab

---
 ...e_generation_and_disposal_practices_.ipynb | 232 ++++++++++++------
 1 file changed, 159 insertions(+), 73 deletions(-)

diff --git a/160124737026_CBIT_IT_1_EDAV_2025_CEP_39_Analyze_urban_municipal_waste_generation_and_disposal_practices_.ipynb b/160124737026_CBIT_IT_1_EDAV_2025_CEP_39_Analyze_urban_municipal_waste_generation_and_disposal_practices_.ipynb
index 3635b9b..d7c960c 100644
--- a/160124737026_CBIT_IT_1_EDAV_2025_CEP_39_Analyze_urban_municipal_waste_generation_and_disposal_practices_.ipynb
+++ b/160124737026_CBIT_IT_1_EDAV_2025_CEP_39_Analyze_urban_municipal_waste_generation_and_disposal_practices_.ipynb
@@ -4,7 +4,6 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyPLegGaufNi7Onh1cxUTEHs",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -67,16 +66,40 @@
         "id": "SslhcBFB6Tdm"
       }
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#**Q1: Calculate average waste generation per capita (BL-3)**"
+      ],
+      "metadata": {
+        "id": "SoVzjNH2-wvC"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The code first identifies the columns containing information about total waste generated and population for each urban body.\n",
+        "\n",
+        "Then, it calculates the waste generated per person by dividing the total waste generated by the population for every record in the dataset.\n",
+        "\n",
+        "After that, it computes the mean (average) of these per-person values across all rows (or optionally by year).\n",
+        "\n",
+        "This gives one combined metric called “Average Waste Generation Per Capita”, which represents the average amount of waste produced by each individual in the dataset or city."
+      ],
+      "metadata": {
+        "id": "_fJ8qoLU_FGu"
+      }
+    },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 2,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 342
+          "height": 340
         },
         "id": "HibBUVg61JTj",
-        "outputId": "4d11209a-a58b-4dfc-e191-844bbdd1bde7"
+        "outputId": "fc2fc5b9-1705-46ce-8716-07b1c2a75d64"
       },
       "outputs": [
         {
@@ -87,9 +110,9 @@
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               "        style=\"border:none\" />\n",
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@@ -296,7 +319,7 @@
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               "\n",
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@@ -413,12 +436,12 @@
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               "      buttonEl.style.display =\n",
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               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-78e05f4e-2814-435b-9590-8e6d2e8bb3ed');\n",
+              "        const element = document.querySelector('#df-5af3fbcf-751d-4cff-b8a6-dd26ad444f6b');\n",
               "        const dataTable =\n",
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@@ -438,8 +461,8 @@
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@@ -558,14 +581,14 @@
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@@ -609,7 +632,7 @@
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               "\n",
@@ -658,25 +681,25 @@
     {
       "cell_type": "markdown",
       "source": [
-        "#**Q1: Calculate average waste generation per capita (BL-3)**"
+        "#**Q2: Filter waste data by waste type (BL-3)**"
       ],
       "metadata": {
-        "id": "SoVzjNH2-wvC"
+        "id": "j_AMRDaV_qy3"
       }
     },
     {
       "cell_type": "markdown",
       "source": [
-        "The code first identifies the columns containing information about total waste generated and population for each urban body.\n",
+        "Explanation\n",
         "\n",
-        "Then, it calculates the waste generated per person by dividing the total waste generated by the population for every record in the dataset.\n",
+        "The code first identifies the column that contains the type of waste (for example, Plastic, Biodegradable, E-Waste, etc.).\n",
         "\n",
-        "After that, it computes the mean (average) of these per-person values across all rows (or optionally by year).\n",
+        "Then, it applies a filter condition to select only those rows where the waste type matches the chosen category (e.g., 'Plastic').\n",
         "\n",
-        "This gives one combined metric called “Average Waste Generation Per Capita”, which represents the average amount of waste produced by each individual in the dataset or city."
+        "This process isolates specific data related to one waste category, allowing focused analysis on how that particular type of waste is generated, managed, or disposed of across different urban bodies or years."
       ],
       "metadata": {
-        "id": "_fJ8qoLU_FGu"
+        "id": "rdtnbovR_5U1"
       }
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@@ -698,12 +721,12 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
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+          "height": 224
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-      "execution_count": null,
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       "outputs": [
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@@ -732,7 +755,7 @@
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               "\n",
-              "  <div id=\"df-c0749046-0f16-4323-a8a1-d827da37d9a6\" class=\"colab-df-container\">\n",
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@@ -823,7 +846,7 @@
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@@ -875,12 +898,12 @@
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+              "        document.querySelector('#df-b6d7d0cb-934e-47a6-9a37-44ea4f5f3c86 button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
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               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-c0749046-0f16-4323-a8a1-d827da37d9a6');\n",
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               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -900,8 +923,8 @@
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@@ -1020,7 +1043,7 @@
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@@ -1042,10 +1065,10 @@
     {
       "cell_type": "markdown",
       "source": [
-        "#**Q2: Filter waste data by waste type (BL-3)**"
+        "#**Q3: Impute missing waste disposal records (BL-4)**"
       ],
       "metadata": {
-        "id": "j_AMRDaV_qy3"
+        "id": "wFh32proAGVo"
       }
     },
     {
@@ -1053,14 +1076,16 @@
       "source": [
         "Explanation\n",
         "\n",
-        "The code first identifies the column that contains the type of waste (for example, Plastic, Biodegradable, E-Waste, etc.).\n",
+        "The code first checks for missing values in the columns related to waste disposal — such as waste_disposed and disposal_method.\n",
         "\n",
-        "Then, it applies a filter condition to select only those rows where the waste type matches the chosen category (e.g., 'Plastic').\n",
+        "Then, it fills (imputes) the missing numerical values in waste_disposed using the median of similar records grouped by factors like urban body, waste type, and year.\n",
         "\n",
-        "This process isolates specific data related to one waste category, allowing focused analysis on how that particular type of waste is generated, managed, or disposed of across different urban bodies or years."
+        "For missing categorical values in disposal_method, it replaces them with the most common (mode) method used within the same group.\n",
+        "\n",
+        "This ensures that the dataset becomes complete and consistent, allowing accurate analysis without losing valuable records due to missing data."
       ],
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               "            style=\"display:none;\">\n",
               "\n",
@@ -1299,12 +1324,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-6054fb11-87a5-46bc-b55f-cbabcd4740a8 button.colab-df-convert');\n",
+              "        document.querySelector('#df-886278f9-145e-477b-abc1-0b611c6cabdd button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-6054fb11-87a5-46bc-b55f-cbabcd4740a8');\n",
+              "        const element = document.querySelector('#df-886278f9-145e-477b-abc1-0b611c6cabdd');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -1324,8 +1349,8 @@
               "  </div>\n",
               "\n",
               "\n",
-              "    <div id=\"df-740cd342-9cb4-47a0-8c48-6494a8b48413\">\n",
-              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-740cd342-9cb4-47a0-8c48-6494a8b48413')\"\n",
+              "    <div id=\"df-17e169d6-1e14-438d-88a0-edf695c2ee54\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-17e169d6-1e14-438d-88a0-edf695c2ee54')\"\n",
               "                title=\"Suggest charts\"\n",
               "                style=\"display:none;\">\n",
               "\n",
@@ -1444,7 +1469,7 @@
               "        }\n",
               "        (() => {\n",
               "          let quickchartButtonEl =\n",
-              "            document.querySelector('#df-740cd342-9cb4-47a0-8c48-6494a8b48413 button');\n",
+              "            document.querySelector('#df-17e169d6-1e14-438d-88a0-edf695c2ee54 button');\n",
               "          quickchartButtonEl.style.display =\n",
               "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "        })();\n",
@@ -1466,10 +1491,10 @@
     {
       "cell_type": "markdown",
       "source": [
-        "#**Q3: Impute missing waste disposal records (BL-4)**"
+        "#**Q4: Group waste data by urban bodies (BL-4)**"
       ],
       "metadata": {
-        "id": "wFh32proAGVo"
+        "id": "SGCtKz0oAX_H"
       }
     },
     {
@@ -1477,16 +1502,16 @@
       "source": [
         "Explanation\n",
         "\n",
-        "The code first checks for missing values in the columns related to waste disposal — such as waste_disposed and disposal_method.\n",
+        "The code groups the dataset by the urban body column, which represents different cities or municipalities.\n",
         "\n",
-        "Then, it fills (imputes) the missing numerical values in waste_disposed using the median of similar records grouped by factors like urban body, waste type, and year.\n",
+        "Then, it calculates aggregate values for each group — such as the total waste generated, total waste disposed, and average collection efficiency.\n",
         "\n",
-        "For missing categorical values in disposal_method, it replaces them with the most common (mode) method used within the same group.\n",
+        "It also computes per capita waste generation by dividing the total waste generated by the latest recorded population of each urban body.\n",
         "\n",
-        "This ensures that the dataset becomes complete and consistent, allowing accurate analysis without losing valuable records due to missing data."
+        "This grouping provides a summary view of waste management performance for each city, making it easier to compare efficiency and output across different urban areas."
       ],
       "metadata": {
-        "id": "5qCjAJbVAR1z"
+        "id": "S8QAFd5cAhFz"
       }
     },
     {
@@ -1517,9 +1542,9 @@
           "height": 363
         },
         "id": "Ly5tEYaC2LNm",
-        "outputId": "ba96c6df-2270-4542-cc0f-0c203d336069"
+        "outputId": "c7b0ce57-e4eb-46ce-f715-7390de0c6e86"
       },
-      "execution_count": null,
+      "execution_count": 5,
       "outputs": [
         {
           "output_type": "display_data",
@@ -1551,7 +1576,7 @@
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-ac62c109-c780-409d-9efa-79a6dc70df7e\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-e9502248-1486-444d-875c-cb6dffd455b6\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -1675,7 +1700,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ac62c109-c780-409d-9efa-79a6dc70df7e')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e9502248-1486-444d-875c-cb6dffd455b6')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -1727,12 +1752,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-ac62c109-c780-409d-9efa-79a6dc70df7e button.colab-df-convert');\n",
+              "        document.querySelector('#df-e9502248-1486-444d-875c-cb6dffd455b6 button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-ac62c109-c780-409d-9efa-79a6dc70df7e');\n",
+              "        const element = document.querySelector('#df-e9502248-1486-444d-875c-cb6dffd455b6');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -1752,8 +1777,8 @@
               "  </div>\n",
               "\n",
               "\n",
-              "    <div id=\"df-d1f80a6a-1445-4790-b699-36bb80bfeccb\">\n",
-              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d1f80a6a-1445-4790-b699-36bb80bfeccb')\"\n",
+              "    <div id=\"df-da05cca2-4035-462f-a651-e68e053670fa\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-da05cca2-4035-462f-a651-e68e053670fa')\"\n",
               "                title=\"Suggest charts\"\n",
               "                style=\"display:none;\">\n",
               "\n",
@@ -1872,7 +1897,7 @@
               "        }\n",
               "        (() => {\n",
               "          let quickchartButtonEl =\n",
-              "            document.querySelector('#df-d1f80a6a-1445-4790-b699-36bb80bfeccb button');\n",
+              "            document.querySelector('#df-da05cca2-4035-462f-a651-e68e053670fa button');\n",
               "          quickchartButtonEl.style.display =\n",
               "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "        })();\n",
@@ -1894,28 +1919,89 @@
     {
       "cell_type": "markdown",
       "source": [
-        "#**Q4: Group waste data by urban bodies (BL-4)**"
+        "#**Q5: Visualize trends in waste generation and disposal (BL-5)**"
       ],
       "metadata": {
-        "id": "SGCtKz0oAX_H"
+        "id": "0qHZ8_gNU-X1"
       }
     },
     {
       "cell_type": "markdown",
       "source": [
-        "Explanation\n",
+        "The code analyzes how waste generation and disposal have changed over time for each urban body. It first aggregates the total waste generated and disposed for every city in each year. Then, it creates clear line plots showing trends — one for total waste generated and another for total waste disposed. If disposal data is missing, it skips that graph and informs the user. Finally, it plots overall trends across all cities combined, helping visualize whether waste disposal keeps pace with generation and showing changes in waste management efficiency over the years."
+      ],
+      "metadata": {
+        "id": "hGlM_OUZVCjz"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
         "\n",
-        "The code groups the dataset by the urban body column, which represents different cities or municipalities.\n",
+        "# Load dataset\n",
+        "CSV_PATH = '/content/urban_waste_data.csv'\n",
+        "df = pd.read_csv(CSV_PATH)\n",
         "\n",
-        "Then, it calculates aggregate values for each group — such as the total waste generated, total waste disposed, and average collection efficiency.\n",
+        "# Q5: Trend visualization\n",
+        "SPLIT_BY_URBAN_BODY = False  # change to True to see each city separately\n",
         "\n",
-        "It also computes per capita waste generation by dividing the total waste generated by the latest recorded population of each urban body.\n",
+        "# 1) Overall totals by year\n",
+        "yearly = df.groupby('year', as_index=False).agg(\n",
+        "    total_generated=('total_waste_generated', 'sum'),\n",
+        "    total_disposed=('waste_disposed', 'sum')\n",
+        ")\n",
         "\n",
-        "This grouping provides a summary view of waste management performance for each city, making it easier to compare efficiency and output across different urban areas."
+        "# Plot overall trends\n",
+        "plt.figure()\n",
+        "plt.plot(yearly['year'], yearly['total_generated'], marker='o', label='Total Generated')\n",
+        "plt.plot(yearly['year'], yearly['total_disposed'], marker='o', label='Total Disposed')\n",
+        "plt.title('Overall Waste Generation vs Disposal Over Time')\n",
+        "plt.xlabel('Year')\n",
+        "plt.ylabel('Tonnage')\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "# 2) Optional: per-urban-body plots\n",
+        "if SPLIT_BY_URBAN_BODY:\n",
+        "    yearly_city = df.groupby(['urban_body','year'], as_index=False).agg(\n",
+        "        total_generated=('total_waste_generated', 'sum'),\n",
+        "        total_disposed=('waste_disposed', 'sum')\n",
+        "    )\n",
+        "    for metric in ['total_generated', 'total_disposed']:\n",
+        "        plt.figure()\n",
+        "        for city, grp in yearly_city.groupby('urban_body'):\n",
+        "            plt.plot(grp['year'], grp[metric], marker='o', label=city)\n",
+        "        plt.title(f'{metric.replace(\"_\", \" \").title()} by Urban Body Over Time')\n",
+        "        plt.xlabel('Year')\n",
+        "        plt.ylabel('Tonnage')\n",
+        "        plt.legend()\n",
+        "        plt.grid(True)\n",
+        "        plt.show()"
       ],
       "metadata": {
-        "id": "S8QAFd5cAhFz"
-      }
+        "id": "5F-fAtgmVHTE",
+        "outputId": "6f0e0e79-c7ae-4c00-974a-64bbb1b7b253",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        }
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -1956,7 +2042,7 @@
       "metadata": {
         "id": "udGfNkbvBJOW"
       },
-      "execution_count": null,
+      "execution_count": 6,
       "outputs": []
     }
   ]