veteran_suicide_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "file_path = \"VA_State_Sheets_2001-2022_Appendix_508.xlsx\"\n",
    "df_state = pd.read_excel(file_path, sheet_name=\"Veteran Suicides by State\", skiprows=1)\n",
    "\n",
    "df_state.columns = [\"Year\", \"Geographic Region\", \"State\", \"Veteran Suicides\"]\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "state_encoder = LabelEncoder()\n",
    "df_state[\"State\"] = state_encoder.fit_transform(df_state[\"State\"])\n",
    "region_encoder = LabelEncoder()\n",
    "df_state[\"Geographic Region\"] = region_encoder.fit_transform(df_state[\"Geographic Region\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_state[[\"Year\", \"Geographic Region\", \"State\"]]\n",
    "y = df_state[\"Veteran Suicides\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "y_train = pd.to_numeric(y_train, errors=\"coerce\")\n",
    "y_test = pd.to_numeric(y_test, errors=\"coerce\")\n",
    "\n",
    "y_train.fillna(y_train.median(), inplace=True)\n",
    "y_test.fillna(y_test.median(), inplace=True)\n",
    "\n",
    "y_train = np.array(y_train, dtype=np.float64)\n",
    "y_test = np.array(y_test, dtype=np.float64)\n",
    "\n",
    "print(\"y_train unique values:\", np.unique(y_train))  # "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "model = keras.Sequential([\n",
    "    layers.Dense(64, activation=\"relu\", input_shape=(X_train.shape[1],)),  # Input layer\n",
    "    layers.Dense(32, activation=\"relu\"),  # Hidden layer\n",
    "    layers.Dense(1)  # Output layer (Regression)\n",
    "])\n",
    "\n",
    "model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])\n",
    "\n",
    "history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=100, batch_size=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss, mae = model.evaluate(X_test, y_test)\n",
    "print(f\"Test Loss: {loss:.2f}, Test MAE: {mae:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(history.history[\"loss\"], label=\"Training Loss\")\n",
    "plt.plot(history.history[\"val_loss\"], label=\"Validation Loss\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss (MSE)\")\n",
    "plt.legend()\n",
    "plt.title(\"Training vs Validation Loss\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(history.history[\"mae\"], label=\"Training MAE\")\n",
    "plt.plot(history.history[\"val_mae\"], label=\"Validation MAE\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Mean Absolute Error (MAE)\")\n",
    "plt.legend()\n",
    "plt.title(\"Training vs Validation MAE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "new_data = pd.DataFrame([[2023, region_encoder.transform([\"Northeastern\"])[0], state_encoder.transform([\"New York\"])[0]]], \n",
    "                        columns=[\"Year\", \"Geographic Region\", \"State\"])\n",
    "\n",
    "new_data = scaler.transform(new_data)\n",
    "\n",
    "prediction = model.predict(new_data)\n",
    "print(f\"Predicted Veteran Suicides: {int(prediction[0][0])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss, mae = model.evaluate(X_test, y_test)\n",
    "print(f\"Test Loss (MSE): {loss:.2f}, Test MAE: {mae:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(history.history[\"loss\"], label=\"Training Loss\")\n",
    "plt.plot(history.history[\"val_loss\"], label=\"Validation Loss\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss (MSE)\")\n",
    "plt.legend()\n",
    "plt.title(\"Training vs Validation Loss\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(history.history[\"mae\"], label=\"Training MAE\")\n",
    "plt.plot(history.history[\"val_mae\"], label=\"Validation MAE\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Mean Absolute Error (MAE)\")\n",
    "plt.legend()\n",
    "plt.title(\"Training vs Validation MAE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"veteran_suicide_model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.losses import MeanSquaredError\n",
    "\n",
    "model = load_model(\"veteran_suicide_model.h5\", custom_objects={\"mse\": MeanSquaredError()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=\"adam\", loss=\"mse\", metrics=[\"mae\"])\n",
    "\n",
    "loss, mae = model.evaluate(X_test, y_test)\n",
    "print(f\"Test Loss (MSE): {loss:.2f}, Test MAE: {mae:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(history.history[\"loss\"], label=\"Training Loss\")\n",
    "plt.plot(history.history[\"val_loss\"], label=\"Validation Loss\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss (MSE)\")\n",
    "plt.legend()\n",
    "plt.title(\"Training vs Validation Loss\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(history.history[\"mae\"], label=\"Training MAE\")\n",
    "plt.plot(history.history[\"val_mae\"], label=\"Validation MAE\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Mean Absolute Error (MAE)\")\n",
    "plt.legend()\n",
    "plt.title(\"Training vs Validation MAE\")\n",
    "plt.show()"
   ]
  }
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