diff --git a/PDFM_embeddings_to_predict_PM2_5_in_US.ipynb b/PDFM_embeddings_to_predict_PM2_5_in_US.ipynb
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@@ -0,0 +1,546 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "view-in-github"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/anindabitm/PDFM-use-case/blob/main/PDFM_embeddings_to_predict_PM2_5_in_US.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "tE3akitQdA-m"
+   },
+   "source": [
+    "**Predicting US PM2.5 levels using Google's Population Dynamics Foundation Model**\n",
+    "\n",
+    "Useful Resources:\n",
+    "1. https://github.com/opengeos/GeoAI-Tutorials/blob/main/docs/PDFM/zillow_home_value.ipynb\n",
+    "2. https://github.com/google-research/population-dynamics/tree/master/notebooks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "TbTs9lKqddKS"
+   },
+   "source": [
+    "Acknowledgements:\n",
+    "This notebook is based on tutorials - [PDFM notebook](https://github.com/google-research/population-dynamics/tree/master/notebooks) and awesome tutorial by giswqs opengeos PDFM [zillow home price](https://github.com/opengeos/GeoAI-Tutorials/blob/main/docs/PDFM/zillow_home_value.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "JqjIF4kAKZGR"
+   },
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "!pip install leafmap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "lIYdn1woOS1n"
+   },
+   "outputs": [],
+   "source": [
+    "# import libraries\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "from leafmap.common import evaluate_model, plot_actual_vs_predicted, download_file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0z3FKT1fgePa"
+   },
+   "source": [
+    "# Get US PM2.5 data\n",
+    "Link to data: https://usc-geohealth-hub-uscssi.hub.arcgis.com/documents/7fc448343d6643f3bb13157fd65aed4f/about"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "8VYkB_TeA0kP"
+   },
+   "outputs": [],
+   "source": [
+    "df0 = pd.read_excel(\"/content/pm25_and_disparity.xlsx\", sheet_name=\"data_part1\")\n",
+    "df1 = pd.read_excel(\"/content/pm25_and_disparity.xlsx\", sheet_name=\"data_part2\")\n",
+    "df2 = pd.read_excel(\"/content/pm25_and_disparity.xlsx\", sheet_name=\"data_part3\")\n",
+    "df3 = pd.read_excel(\"/content/pm25_and_disparity.xlsx\", sheet_name=\"data_part4\")\n",
+    "df4 = pd.read_excel(\"/content/pm25_and_disparity.xlsx\", sheet_name=\"data_part5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GYC_GWkdgodj"
+   },
+   "source": [
+    "# Process PM2.5 data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "QE3MZiRYA6q1",
+    "outputId": "b194649f-7e4a-4dfe-e1ed-8bc825fd5cb5"
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.concat([df0, df1, df2, df3, df4], ignore_index=True)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "p9NCYMyxGBQB",
+    "outputId": "530a1697-c230-4199-d9f2-1cd07130d07e"
+   },
+   "outputs": [],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ip7NBwl-EDsf",
+    "outputId": "4f5b299d-b6a3-4859-fa0a-f14b96bf3221"
+   },
+   "outputs": [],
+   "source": [
+    "df[\"zcta\"].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 272
+    },
+    "id": "8qtI9VgiCcvj",
+    "outputId": "c2365f08-5de6-45aa-a4fb-1d5b3f7a86a4"
+   },
+   "outputs": [],
+   "source": [
+    "pm25_df = df.groupby([\"zcta\"]).mean()[\"pm25\"]\n",
+    "pm25_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 272
+    },
+    "id": "6-qSy8lVDDoM",
+    "outputId": "1f086442-221a-4f64-ee9d-db456bb21808"
+   },
+   "outputs": [],
+   "source": [
+    "pm25_df.dropna(axis=0, inplace=True)\n",
+    "pm25_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 290
+    },
+    "id": "5kAxQ3uVGswD",
+    "outputId": "5c326987-a0c0-4f4b-f6ec-8094288375c3"
+   },
+   "outputs": [],
+   "source": [
+    "pm25_df.index = pm25_df.index.astype(int)\n",
+    "print(pm25_df.shape)\n",
+    "pm25_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 238
+    },
+    "id": "dBOUhYaOIU0-",
+    "outputId": "32a40749-015a-4d2a-b27f-0ac61bf8b7dc"
+   },
+   "outputs": [],
+   "source": [
+    "pm25_df = pm25_df.reset_index(drop=False)  # Remove inplace=True\n",
+    "pm25_df.index = pm25_df[\"zcta\"].apply(lambda x: f\"zip/{x}\")  # Access 'zcta' column\n",
+    "pm25_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0CUwjIxaeWCm"
+   },
+   "source": [
+    "# Request access to PDFM Embeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "yxEkU1GjibgC",
+    "outputId": "b0a29cdb-1b14-49d1-9302-604ca2ead218"
+   },
+   "outputs": [],
+   "source": [
+    "!unzip /content/pdfm_embeddings.zip"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "-DugeFaSG2Pi"
+   },
+   "outputs": [],
+   "source": [
+    "embeddings_file_path = \"/content/pdfm_embeddings/v0/us/zcta_embeddings.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "JZZX7tzlHYa9"
+   },
+   "outputs": [],
+   "source": [
+    "if not os.path.exists(embeddings_file_path):\n",
+    "    raise FileNotFoundError(\"Please request the embeddings from Google\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 374
+    },
+    "id": "cWNluHJYHY57",
+    "outputId": "3d62af41-8d41-40c7-ab82-5b9174406cfd"
+   },
+   "outputs": [],
+   "source": [
+    "zipcode_embeddings = pd.read_csv(embeddings_file_path).set_index(\"place\")\n",
+    "zipcode_embeddings.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "L7XAkE0fecVU"
+   },
+   "source": [
+    "# Join PDFM embeddings and Groud Truth (PM2.5 data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 429
+    },
+    "id": "KjwmUj5SH9-P",
+    "outputId": "626b6758-821c-486e-9f2b-0b9f3366f433"
+   },
+   "outputs": [],
+   "source": [
+    "data = pm25_df.join(zipcode_embeddings, how=\"inner\")\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "UUKLoxY6JkUt",
+    "outputId": "6518e2c5-8dff-4d07-851f-c486f2da50b2"
+   },
+   "outputs": [],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "NvHQ97WYJmOd"
+   },
+   "outputs": [],
+   "source": [
+    "embedding_features = [f\"feature{x}\" for x in range(330)]\n",
+    "label = \"pm25\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "35igKORxJ15A"
+   },
+   "outputs": [],
+   "source": [
+    "data = data.dropna(subset=[label])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "tuCYm8dcevqo"
+   },
+   "source": [
+    "# Split Train and Test Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "IgSmPhJTJ2QW"
+   },
+   "outputs": [],
+   "source": [
+    "data = data[embedding_features + [label]]\n",
+    "X = data[embedding_features]\n",
+    "y = data[label]\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X, y, test_size=0.2, random_state=42\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "iHys75z1fFFJ"
+   },
+   "source": [
+    "# Fit K-Nearest Neighbors Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "KCgs-cyoJ5Nm"
+   },
+   "outputs": [],
+   "source": [
+    "k = 5\n",
+    "model = KNeighborsRegressor(n_neighbors=k)\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TkWTkqPCKEYu",
+    "outputId": "01404579-141a-41c3-e7ac-d1576130f5f9"
+   },
+   "outputs": [],
+   "source": [
+    "evaluation_df = pd.DataFrame({\"y\": y_test, \"y_pred\": y_pred})\n",
+    "# Evaluate the model\n",
+    "metrics = evaluate_model(evaluation_df)\n",
+    "print(metrics)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ywbC7hLSfMZB"
+   },
+   "source": [
+    "# Evaluate K-Nearest Neighbors Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 817
+    },
+    "id": "LZORS0JQKjQ4",
+    "outputId": "05aa4e15-7008-4663-a364-4406ef00a5a3"
+   },
+   "outputs": [],
+   "source": [
+    "xy_lim = (0, 30)\n",
+    "plot_actual_vs_predicted(\n",
+    "    evaluation_df,\n",
+    "    xlim=xy_lim,\n",
+    "    ylim=xy_lim,\n",
+    "    title=\"Actual vs Predicted PM2.5\",\n",
+    "    x_label=\"Actual PM2.5\",\n",
+    "    y_label=\"Predicted PM2.5\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "SjOVAvPYkDYx"
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LPXkNlenjGp_"
+   },
+   "source": [
+    "# Fit Random Forest Regressor model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "M7S4naYnjN4l",
+    "outputId": "55c7d7d8-b304-4fc2-d8f6-5ed014577bf9"
+   },
+   "outputs": [],
+   "source": [
+    "model = RandomForestRegressor(n_estimators=10, verbose=10, n_jobs=-1)\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "CaxFfeEtjRiH",
+    "outputId": "590e443f-6f93-46d9-ccfe-d1723600d0a3"
+   },
+   "outputs": [],
+   "source": [
+    "evaluation_df = pd.DataFrame({\"y\": y_test, \"y_pred\": y_pred})\n",
+    "# Evaluate the model\n",
+    "metrics = evaluate_model(evaluation_df)\n",
+    "print(metrics)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "v96jVyGRjWtE"
+   },
+   "source": [
+    "# Evaluate Random Forest Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 817
+    },
+    "id": "bVnYyd-zjb85",
+    "outputId": "76b5b25d-a9d7-4c16-c1d7-015e814f278d"
+   },
+   "outputs": [],
+   "source": [
+    "xy_lim = (0, 30)\n",
+    "plot_actual_vs_predicted(\n",
+    "    evaluation_df,\n",
+    "    xlim=xy_lim,\n",
+    "    ylim=xy_lim,\n",
+    "    title=\"Actual vs Predicted PM2.5\",\n",
+    "    x_label=\"Actual PM2.5\",\n",
+    "    y_label=\"Predicted PM2.5\",\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "include_colab_link": true,
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}