diff --git a/Matplotlib/Matplotlib_Correlation_Heatmap.ipynb b/Matplotlib/Matplotlib_Correlation_Heatmap.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cab73e9a-9e42-45dd-91cb-8d7d203d734c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-16T17:37:56.670971Z",
+     "iopub.status.busy": "2023-10-16T17:37:56.670500Z",
+     "iopub.status.idle": "2023-10-16T17:37:56.681790Z",
+     "shell.execute_reply": "2023-10-16T17:37:56.680945Z",
+     "shell.execute_reply.started": "2023-10-16T17:37:56.670886Z"
+    },
+    "tags": []
+   },
+   "source": [
+    "<img width=\"10%\" alt=\"Naas\" src=\"https://landen.imgix.net/jtci2pxwjczr/assets/5ice39g4.png?w=160\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "edb9d0c3-fd59-4e5b-b86f-4612069d386c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-16T17:41:03.383023Z",
+     "iopub.status.busy": "2023-10-16T17:41:03.382701Z",
+     "iopub.status.idle": "2023-10-16T17:41:03.402250Z",
+     "shell.execute_reply": "2023-10-16T17:41:03.401286Z",
+     "shell.execute_reply.started": "2023-10-16T17:41:03.382985Z"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Matplotlib- Correlation Heatmap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2881fa65-31d4-48ec-b7ec-33feace608e2",
+   "metadata": {},
+   "source": [
+    "**Tags:** #matplotlib #correlation #heatmap #dataviz #snippet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c18d244-aeab-406a-9f1a-450f9cfa51f3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-16T17:43:00.717079Z",
+     "iopub.status.busy": "2023-10-16T17:43:00.716824Z",
+     "iopub.status.idle": "2023-10-16T17:43:00.727560Z",
+     "shell.execute_reply": "2023-10-16T17:43:00.726568Z",
+     "shell.execute_reply.started": "2023-10-16T17:43:00.717042Z"
+    }
+   },
+   "source": [
+    "**Author:** [Rittika Deb](https://www.linkedin.com/in/rittika-deb/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d758e605-8691-42d0-bbea-154cb64a83ef",
+   "metadata": {},
+   "source": [
+    "**Last update:** 2023-10-17 (Created: 2023-10-17)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c357d9d5-7473-467f-82f7-535c71bda7f7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-16T17:53:17.127826Z",
+     "iopub.status.busy": "2023-10-16T17:53:17.127606Z",
+     "iopub.status.idle": "2023-10-16T17:53:17.137291Z",
+     "shell.execute_reply": "2023-10-16T17:53:17.136412Z",
+     "shell.execute_reply.started": "2023-10-16T17:53:17.127802Z"
+    }
+   },
+   "source": [
+    "**Description:** This template will create a correlation heatmap. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a985c7d5-e713-4df4-a5cc-a7e07f28b45d",
+   "metadata": {},
+   "source": [
+    "## Input"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "053efcee-5abc-4261-9c3f-9b07335fce37",
+   "metadata": {},
+   "source": [
+    "### Import libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "997f305a-2f31-43e9-8be6-4e9ca18a42f9",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-17T19:00:27.439866Z",
+     "iopub.status.busy": "2023-10-17T19:00:27.439512Z",
+     "iopub.status.idle": "2023-10-17T19:00:29.164570Z",
+     "shell.execute_reply": "2023-10-17T19:00:29.163862Z",
+     "shell.execute_reply.started": "2023-10-17T19:00:27.439775Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c8863f9-7485-4249-9355-ccba68b273d6",
+   "metadata": {},
+   "source": [
+    "### Setup variables\n",
+    "- `data`: An array representing an 8x8 grid of data points.\n",
+    "- `xlabs and ylabs`: Lists of strings that label the rows and columns of the heatmap.\n",
+    "- `fig`: Top-level container that holds everything in the figure.\n",
+    "- `ax`: Axis object where you can plot your data.\n",
+    "- `heatmap`: Actual heatmap representation of the data.\n",
+    "- `ax.set_xticks and ax.set_yticks`: To set the tick positions along the x and y axes, respectively. \n",
+    "- `ax.set_xticklabels and ax.set_yticklabels`: These methods set the tick labels for the x and y axes, respectively.\n",
+    "- `plt.colorbar(heatmap)`: Adds a colorbar to the heatmap to indicate the color mapping of values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "cbf14455-b245-4837-a17d-46a6863f6543",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-16T19:25:44.835351Z",
+     "iopub.status.busy": "2023-10-16T19:25:44.835120Z",
+     "iopub.status.idle": "2023-10-16T19:25:44.838820Z",
+     "shell.execute_reply": "2023-10-16T19:25:44.838198Z",
+     "shell.execute_reply.started": "2023-10-16T19:25:44.835327Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "data = {\n",
+    "    'A': np.random.rand(100),\n",
+    "    'B': np.random.rand(100),\n",
+    "    'C': np.random.rand(100),\n",
+    "    'D': np.random.rand(100)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "ba4aa346-3ac0-4529-b897-33a2e5b00a96",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-16T19:25:48.030371Z",
+     "iopub.status.busy": "2023-10-16T19:25:48.030146Z",
+     "iopub.status.idle": "2023-10-16T19:25:48.036872Z",
+     "shell.execute_reply": "2023-10-16T19:25:48.036321Z",
+     "shell.execute_reply.started": "2023-10-16T19:25:48.030348Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a38219b-cc92-47c2-a6ce-0e46c52da4e1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-16T19:12:24.550584Z",
+     "iopub.status.busy": "2023-10-16T19:12:24.550358Z",
+     "iopub.status.idle": "2023-10-16T19:12:24.553470Z",
+     "shell.execute_reply": "2023-10-16T19:12:24.552819Z",
+     "shell.execute_reply.started": "2023-10-16T19:12:24.550561Z"
+    }
+   },
+   "source": [
+    "## Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "a3efb78f-8124-4393-a883-473d2743296a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-16T19:25:49.435376Z",
+     "iopub.status.busy": "2023-10-16T19:25:49.435155Z",
+     "iopub.status.idle": "2023-10-16T19:25:49.439089Z",
+     "shell.execute_reply": "2023-10-16T19:25:49.438391Z",
+     "shell.execute_reply.started": "2023-10-16T19:25:49.435353Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "correlation_matrix = df.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "d7d33bc4-e1b4-4d8e-b4d4-0d1e78f45d5d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-16T19:25:50.061146Z",
+     "iopub.status.busy": "2023-10-16T19:25:50.060595Z",
+     "iopub.status.idle": "2023-10-16T19:25:50.068156Z",
+     "shell.execute_reply": "2023-10-16T19:25:50.067487Z",
+     "shell.execute_reply.started": "2023-10-16T19:25:50.061109Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "xlabs = correlation_matrix.columns\n",
+    "ylabs = correlation_matrix.index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f83319a-99d3-4b17-aedf-c1f923d8b904",
+   "metadata": {},
+   "source": [
+    "## Output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5ebe3d71-a044-4fed-8d3d-a6bd7bc67a02",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-10-16T19:25:53.050436Z",
+     "iopub.status.busy": "2023-10-16T19:25:53.050214Z",
+     "iopub.status.idle": "2023-10-16T19:25:53.231261Z",
+     "shell.execute_reply": "2023-10-16T19:25:53.230644Z",
+     "shell.execute_reply.started": "2023-10-16T19:25:53.050413Z"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axis.YTick at 0x7f664b842730>,\n",
+       "  <matplotlib.axis.YTick at 0x7f664b842490>,\n",
+       "  <matplotlib.axis.YTick at 0x7f664b8eabe0>,\n",
+       "  <matplotlib.axis.YTick at 0x7f664b9df160>],\n",
+       " [Text(0, 0, 'A'), Text(0, 1, 'B'), Text(0, 2, 'C'), Text(0, 3, 'D')])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 576x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.imshow(correlation_matrix, cmap='coolwarm', interpolation='none', aspect='auto')\n",
+    "plt.colorbar()\n",
+    "plt.title('Correlation Heatmap')\n",
+    "plt.xticks(np.arange(len(xlabs)), xlabs, rotation=90)\n",
+    "plt.yticks(np.arange(len(ylabs)), ylabs)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fcc3af40",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}