code/part_5_data_modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6baf22ac-f180-4b62-b48a-24fb0d11e342",
   "metadata": {},
   "source": [
    "# ![](https://ga-dash.s3.amazonaws.com/production/assets/logo-9f88ae6c9c3871690e33280fcf557f33.png) Group Project by Dolphin Sharma and Charles Crocicchia\n",
    "\n",
    "## Data Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f77d707b-3fd7-46f5-8945-3bc96d981dde",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mticker\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score, train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "480fc71c-76f5-4970-99aa-1d3142c2979b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Directories for data and plots\n",
    "data_paths = {\n",
    "    'ridge': '../data/processed_data/linear_regression_data.csv',\n",
    "    'decision_tree': '../data/processed_data/decision_tree_data.csv',\n",
    "    'random_forest': '../data/processed_data/random_forest_data.csv'\n",
    "}\n",
    "\n",
    "plots_directory = '../plots/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "14d45047-abb9-4301-8312-ca1bf20b19f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Features and targets\n",
    "features = [\n",
    "    'Corn_Acres_Planted', 'Soybean_Acres_Planted', 'Corn_Cash_Receipts',\n",
    "    'Corn_Production_Bushels', 'Corn_Production_Dollars', 'Corn_Yield_Bushels',\n",
    "    'Soybean_Cash_Receipts', 'Soybean_Production_Bushels', 'Soybean_Production_Dollars',\n",
    "    'Soybean_Yield_Bushels'\n",
    "]\n",
    "\n",
    "targets = ['Employment_Rural', 'Personal_Income_Rural', 'Poverty_Levels']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8f6413bf-f647-4fd0-b66f-c785be70e3b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "def load_data(path):\n",
    "    return pd.read_csv(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3d4dabac-b08b-4f27-9745-17587db7e4df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare data\n",
    "def prepare_data(data, target):\n",
    "    data_cleaned = data.dropna(subset=features + [target])\n",
    "    X = data_cleaned[features]\n",
    "    y = data_cleaned[target]\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "    return X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2ddfcc70-23fb-47a3-8009-e0e1ed13f1d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train and evaluate model\n",
    "def train_and_evaluate_model(model, param_grid, X_train, X_test, y_train, y_test, target_name):\n",
    "    grid_search = GridSearchCV(model, param_grid, cv=5, scoring='neg_mean_squared_error', n_jobs=-1)\n",
    "    grid_search.fit(X_train, y_train)\n",
    "    best_model = grid_search.best_estimator_\n",
    "    \n",
    "    # Cross-validation RMSE\n",
    "    cv_scores = cross_val_score(best_model, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
    "    mean_cv_rmse = np.sqrt(-cv_scores.mean())\n",
    "\n",
    "    # Test set predictions\n",
    "    y_pred = best_model.predict(X_test)\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "    rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "    \n",
    "    # Print evaluation metrics\n",
    "    print(f\"\\nTarget: {target_name}\")\n",
    "    print(f\"Best Parameters: {grid_search.best_params_}\")\n",
    "    print(f\"Mean CV RMSE: {mean_cv_rmse:.2f}\")\n",
    "    print(f\"R² Score on Test Set: {r2:.2f}\")\n",
    "    print(f\"RMSE on Test Set: {rmse:.2f}\\n\")\n",
    "    \n",
    "    return {\n",
    "        'Best Parameters': grid_search.best_params_,\n",
    "        'R2': r2,\n",
    "        'RMSE': rmse,\n",
    "        'CV_RMSE': mean_cv_rmse,\n",
    "        'y_test': y_test,\n",
    "        'y_pred': y_pred\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9f314d21-72d5-4b6c-9e5b-249b0ed17270",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot evaluation metrics (R², RMSE, and Mean CV RMSE)\n",
    "def plot_evaluation_metrics(results, model_name, plot_dir=plots_directory):\n",
    "    targets_labels = ['Employment', 'Personal Income', 'Poverty Level']\n",
    "    r2_scores = [results[t]['R2'] for t in targets]\n",
    "    rmse_scores = [results[t]['RMSE'] for t in targets]\n",
    "    cv_rmse_scores = [results[t]['CV_RMSE'] for t in targets]\n",
    "    \n",
    "    fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(18, 6))\n",
    "\n",
    "    # Plot R² scores\n",
    "    bars1 = ax1.bar(targets_labels, r2_scores, color='tab:olive')\n",
    "    ax1.set_title('R² Score for Each Target', fontweight='bold')\n",
    "    ax1.set_xlabel('Target Variable', fontstyle='italic')\n",
    "    ax1.set_ylabel('R² Score', fontstyle='italic')\n",
    "    for bar in bars1:\n",
    "        yval = bar.get_height()\n",
    "        ax1.text(bar.get_x() + bar.get_width()/2, yval, round(yval, 2), va='bottom', ha='center')\n",
    "\n",
    "    # Plot RMSE scores\n",
    "    bars2 = ax2.bar(targets_labels, rmse_scores, color='#f1c232')\n",
    "    ax2.set_title('RMSE for Each Target', fontweight='bold')\n",
    "    ax2.set_xlabel('Target Variable', fontstyle='italic')\n",
    "    ax2.set_ylabel('RMSE', fontstyle='italic')\n",
    "    for bar in bars2:\n",
    "        yval = bar.get_height()\n",
    "        ax2.text(bar.get_x() + bar.get_width()/2, yval, round(yval, 2), va='bottom', ha='center')\n",
    "\n",
    "    # Plot Mean CV RMSE scores\n",
    "    bars3 = ax3.bar(targets_labels, cv_rmse_scores, color='#0D98BA')\n",
    "    ax3.set_title('Mean CV RMSE for Each Target', fontweight='bold')\n",
    "    ax3.set_xlabel('Target Variable', fontstyle='italic')\n",
    "    ax3.set_ylabel('Mean CV RMSE', fontstyle='italic')\n",
    "    for bar in bars3:\n",
    "        yval = bar.get_height()\n",
    "        ax3.text(bar.get_x() + bar.get_width()/2, yval, round(yval, 2), va='bottom', ha='center')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plot_path = os.path.join(plot_dir, f'{model_name}_results.png')\n",
    "    plt.savefig(plot_path)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e7a88551-06a0-4c72-beca-18a87cadcd79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot actual vs predicted for each target variable in a single figure with subplots\n",
    "def plot_actual_vs_predicted(y_test_data, y_pred_data, plot_dir=plots_directory):\n",
    "    targets = ['Employment', 'Personal Income', 'Poverty Level']\n",
    "    colors = ['#2986cc', '#8fce00', '#6a329f']\n",
    "    ideal_line_color = '#999999'\n",
    "\n",
    "    # Create subplots for the three targets\n",
    "    fig, axes = plt.subplots(1, 3, figsize=(22, 6))\n",
    "    \n",
    "    for i, (target, y_test, y_pred, color) in enumerate(zip(targets, y_test_data, y_pred_data, colors)):\n",
    "        # Scatter plot of Actual vs Predicted values\n",
    "        axes[i].scatter(y_test, y_test, color=color, label='Actual')\n",
    "        axes[i].scatter(y_test, y_pred, color='#ff7f0e', label='Predicted')\n",
    "        # Ideal line (diagonal)\n",
    "        axes[i].plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], color=ideal_line_color, label='Ideal Line')\n",
    "        \n",
    "        # Title and labels\n",
    "        axes[i].set_title(f'{target}: Actual vs Predicted', fontweight='bold')\n",
    "        axes[i].set_xlabel(f'Actual {target}', fontstyle='italic')\n",
    "        axes[i].set_ylabel(f'Predicted {target}', fontstyle='italic')\n",
    "        \n",
    "        # Specific formatting for larger numbers\n",
    "        if target in targets:\n",
    "            axes[i].get_yaxis().set_major_formatter(mticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "            axes[i].get_xaxis().set_major_formatter(mticker.FuncFormatter(lambda x, p: format(int(x), ',')))\n",
    "        \n",
    "        # Rotate x-axis labels\n",
    "        axes[i].tick_params(axis='x', rotation=45)\n",
    "        \n",
    "        # Legend\n",
    "        axes[i].legend()\n",
    "    \n",
    "    # Adjust layout for cleaner appearance\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(os.path.join(plot_dir, 'actual_vs_predicted.png'))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "66d6a89c-4600-45ba-9161-cb057500fec8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target: Employment_Rural\n",
      "Best Parameters: {'alpha': 0.1}\n",
      "Mean CV RMSE: 0.06\n",
      "R² Score on Test Set: 1.00\n",
      "RMSE on Test Set: 0.04\n",
      "\n",
      "\n",
      "Target: Personal_Income_Rural\n",
      "Best Parameters: {'alpha': 1.0}\n",
      "Mean CV RMSE: 189675.13\n",
      "R² Score on Test Set: 0.98\n",
      "RMSE on Test Set: 82328.87\n",
      "\n",
      "\n",
      "Target: Poverty_Levels\n",
      "Best Parameters: {'alpha': 1.0}\n",
      "Mean CV RMSE: 188802.58\n",
      "R² Score on Test Set: 0.94\n",
      "RMSE on Test Set: 134929.54\n",
      "\n",
      "\n",
      "Target: Employment_Rural\n",
      "Best Parameters: {'max_depth': 10, 'max_features': None, 'min_samples_leaf': 1, 'min_samples_split': 2}\n",
      "Mean CV RMSE: 403710.48\n",
      "R² Score on Test Set: 0.91\n",
      "RMSE on Test Set: 167507.13\n",
      "\n",
      "\n",
      "Target: Personal_Income_Rural\n",
      "Best Parameters: {'max_depth': 10, 'max_features': None, 'min_samples_leaf': 1, 'min_samples_split': 2}\n",
      "Mean CV RMSE: 408630.48\n",
      "R² Score on Test Set: 0.89\n",
      "RMSE on Test Set: 197457.28\n",
      "\n",
      "\n",
      "Target: Poverty_Levels\n",
      "Best Parameters: {'max_depth': 10, 'max_features': None, 'min_samples_leaf': 1, 'min_samples_split': 2}\n",
      "Mean CV RMSE: 513454.16\n",
      "R² Score on Test Set: 0.71\n",
      "RMSE on Test Set: 297702.81\n",
      "\n",
      "\n",
      "Target: Employment_Rural\n",
      "Best Parameters: {'max_depth': 10, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "Mean CV RMSE: 350415.18\n",
      "R² Score on Test Set: 0.97\n",
      "RMSE on Test Set: 90505.22\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\deeps\\anaconda3\\Lib\\site-packages\\numpy\\ma\\core.py:2820: RuntimeWarning: invalid value encountered in cast\n",
      "  _data = np.array(data, dtype=dtype, copy=copy,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target: Personal_Income_Rural\n",
      "Best Parameters: {'max_depth': 10, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "Mean CV RMSE: 341713.84\n",
      "R² Score on Test Set: 0.97\n",
      "RMSE on Test Set: 110522.78\n",
      "\n",
      "\n",
      "Target: Poverty_Levels\n",
      "Best Parameters: {'max_depth': 10, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n",
      "Mean CV RMSE: 430534.24\n",
      "R² Score on Test Set: 0.94\n",
      "RMSE on Test Set: 139691.87\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2200x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define models and parameters\n",
    "models = {\n",
    "    'ridge': (Ridge(), {'alpha': [0.1, 1.0, 10.0, 100.0]}),\n",
    "    'decision_tree': (DecisionTreeRegressor(random_state=42), {\n",
    "        'max_depth': [3, 5, 10, None],\n",
    "        'min_samples_split': [2, 5, 10],\n",
    "        'min_samples_leaf': [1, 2, 4],\n",
    "        'max_features': [None, 'sqrt', 'log2']\n",
    "    }),\n",
    "    'random_forest': (RandomForestRegressor(random_state=42), {\n",
    "        'n_estimators': [100, 200, 300],\n",
    "        'max_depth': [10, 20, None],\n",
    "        'min_samples_split': [2, 5, 10],\n",
    "        'min_samples_leaf': [1, 2, 4],\n",
    "        'max_features': ['sqrt', 'log2', None]\n",
    "    })\n",
    "}\n",
    "\n",
    "# Run models and store results\n",
    "results = {}\n",
    "y_test_rf = []\n",
    "y_pred_rf = []\n",
    "\n",
    "# Step 1: Train and evaluate models in the specified order\n",
    "for model_name, (model, param_grid) in models.items():\n",
    "    data = load_data(data_paths[model_name])\n",
    "    results[model_name] = {}\n",
    "    \n",
    "    for target in targets:\n",
    "        X_train, X_test, y_train, y_test = prepare_data(data, target)\n",
    "        results[model_name][target] = train_and_evaluate_model(model, param_grid, X_train, X_test, y_train, y_test, target)\n",
    "        \n",
    "        # Collect Random Forest results for actual vs predicted plot later\n",
    "        if model_name == 'random_forest':\n",
    "            y_test_rf.append(results[model_name][target]['y_test'])\n",
    "            y_pred_rf.append(results[model_name][target]['y_pred'])\n",
    "\n",
    "# Step 2: Plot evaluation metrics for each model after all models have been run\n",
    "for model_name in models.keys():\n",
    "    plot_evaluation_metrics(results[model_name], model_name)\n",
    "\n",
    "# Step 3: Plot actual vs predicted for Random Forest only after evaluation metrics\n",
    "plot_actual_vs_predicted(y_test_rf, y_pred_rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06282939-37b3-4063-b071-45f203bb3320",
   "metadata": {},
   "source": [
    "### Interpretation\n",
    "\n",
    "#### Ridge Regression\n",
    "\n",
    "1. **Target: Employment_Rural**\n",
    "\n",
    "Test Set Performance:\n",
    "* R² Score: 1.00, which is ideal and suggests the model explains nearly all variability in the target.\n",
    "* RMSE: 0.04, aligning with the cross-validation results and indicating minimal error.\n",
    "* Cross-Validation Scores (Negative MSE): These scores are close to zero, suggesting very low error across folds. The mean CV RMSE is also very low at 0.06, indicating that the model performs consistently across cross-validation splits with minimal error.\n",
    "\n",
    "The model performs exceptionally well for Employment_Rural, with nearly perfect predictions. This suggests a strong linear relationship with the chosen features and minimal overfitting, as the cross-validation and test errors are similarly low.\n",
    "\n",
    "2. **Target: Personal_Income_Rural**\n",
    "\n",
    "Test Set Performance:\n",
    "* R² Score: 0.98, indicating that the model explains 98% of the variability in Personal_Income_Rural, a strong fit but not perfect.\n",
    "* RMSE: 82,328.87, which is lower than the cross-validation RMSE, suggesting the model might generalize better than expected based on the training data alone.\n",
    "* Cross-Validation Scores (Negative MSE): These values are larger in magnitude, indicating more substantial prediction errors. They range from -3.5 billion to -89 billion, showing some variability across folds. The mean CV RMSE is high at 189,675.13, indicating that the model's errors are larger on average across different splits.\n",
    "\n",
    "The model performs well in explaining Personal_Income_Rural but faces higher errors in cross-validation, potentially due to high variance in this target or some outliers. Despite this, the model generalizes well on the test set.\n",
    "\n",
    "3. **Target: Poverty_Levels**\n",
    "\n",
    "Test Set Performance:\n",
    "* R² Score: 0.94, which is still strong, indicating that the model explains 94% of the variability in Poverty_Levels.\n",
    "* RMSE: 134,929.54, higher than for Personal_Income_Rural, suggesting higher prediction errors for Poverty_Levels than for the other targets.\n",
    "* Cross-Validation Scores (Negative MSE): These are also high, ranging from around -4 billion to -106 billion, showing a wide variability across folds. The mean CV RMSE is 188,802.58, suggesting relatively high errors in cross-validation.\n",
    "\n",
    "The model provides a strong fit for Poverty_Levels, but the errors are relatively high, as shown by both cross-validation and test RMSE. This might suggest some data points or regions where the model struggles to generalize, potentially due to complexity or noise in this target variable.\n",
    "\n",
    "**Summary**\n",
    "\n",
    "The best parameter for Ridge regularization was alpha = 1.0 across targets, which balances model complexity with predictive accuracy. The model performed best for Employment_Rural, followed by Personal_Income_Rural and Poverty_Levels, with increasing error levels. Overall, these results indicate that the model explains a significant amount of variance for each target, though there is room for refinement, especially for Poverty_Levels.\n",
    "\n",
    "#### Decision Tree\n",
    "\n",
    "1. **Target: Employment_Rural**\n",
    "\n",
    "Test Set Performance:\n",
    "* R² Score: 0.91, indicating that the model explains 91% of the variance in Employment_Rural, a strong but imperfect fit.\n",
    "* RMSE: 167,507.13, which is lower than the cross-validation RMSE, suggesting that the model may perform better on this specific test set than on the cross-validation splits.\n",
    "* Cross-Validation Scores (Negative MSE): The scores are relatively large, ranging from -1.5 billion to around -346 billion. The high Mean CV RMSE of 403,710.48 suggests significant variation in prediction error across folds and generally higher error for this target.\n",
    "\n",
    "The Decision Tree model has a reasonable fit for Employment_Rural, but the cross-validation error is relatively high, indicating that the model might have difficulty generalizing across different data subsets. The high RMSE suggests that the model may struggle with complexity or overfitting.\n",
    "\n",
    "2. **Target: Personal_Income_Rural**\n",
    "\n",
    "Test Set Performance:\n",
    "* R² Score: 0.89, meaning the model explains 89% of the variance in Personal_Income_Rural, a relatively strong fit but not as high as the Linear Regression model.\n",
    "* RMSE: 197,457.28, which is still substantial, highlighting that the model struggles to predict Personal_Income_Rural with low error.\n",
    "* Cross-Validation Scores (Negative MSE): The cross-validation scores are again large, ranging from -2.2 billion to around -256 billion, with a Mean CV RMSE of 408,630.48, indicating substantial prediction error.\n",
    "\n",
    "For Personal_Income_Rural, the Decision Tree model has a relatively good fit (R² of 0.89), but both cross-validation and test set RMSE scores suggest a high level of error. This could indicate that the model is capturing non-linear relationships but still lacks consistency and precision, possibly due to high data variability or overfitting on particular samples.\n",
    "\n",
    "3. **Target: Poverty_Levels**\n",
    "\n",
    "Test Set Performance:\n",
    "* R² Score: 0.71, meaning the model explains 71% of the variance in Poverty_Levels. This R² is lower than for the other targets, indicating a weaker fit.\n",
    "* RMSE: 297,702.81, which is very high, suggesting a considerable degree of error in predictions on the test set.\n",
    "* Cross-Validation Scores (Negative MSE): These scores vary significantly, from around -9.4 billion to -675 billion, with the highest Mean CV RMSE of 513,454.16 among the three targets.\n",
    "\n",
    "The Decision Tree model appears to struggle the most with Poverty_Levels, as evidenced by both the lower R² score and the high cross-validation and test RMSE values. This could indicate either high data complexity or that the target contains variability that the model is unable to capture effectively, likely due to overfitting on particular data patterns.\n",
    "\n",
    "**Summary**\n",
    "\n",
    "For the Decision Tree model, the best fit is seen with Employment_Rural and Personal_Income_Rural, though the error levels (RMSE) are higher than desired. For Poverty_Levels, both the R² and RMSE scores suggest that the model struggles to generalize effectively. The high cross-validation RMSE for all targets suggests that the model may have overfitting issues, or that there may be outliers or noise impacting its ability to make consistent predictions across different data splits.\n",
    "\n",
    "#### Random Forest\n",
    "\n",
    "1. **Target: Employment_Rural**\n",
    "\n",
    "Test Set Performance:\n",
    "* R² Score: 0.97, meaning the model explains 97% of the variance in Employment_Rural, showing a very strong fit.\n",
    "* RMSE: 90,505.22, which is significantly lower than the cross-validation RMSE, suggesting good generalization to the test set.\n",
    "* Cross-Validation Scores (Negative MSE): The scores range from -423 million to around -418 billion, with a Mean CV RMSE of 350,415.18. This indicates substantial variation across folds, though the error is generally lower compared to the Decision Tree model.\n",
    "\n",
    "The Random Forest model performs very well for Employment_Rural, with a high R² and a relatively low test set RMSE. While cross-validation errors are higher, the model shows better consistency across different data subsets compared to Decision Tree, likely due to Random Forest’s ability to reduce overfitting.\n",
    "\n",
    "2. **Target: Personal_Income_Rural**\n",
    "\n",
    "Test Set Performance:\n",
    "* R² Score: 0.97, indicating the model explains 97% of the variance in Personal_Income_Rural, a strong fit.\n",
    "* RMSE: 110,522.78, lower than the Mean CV RMSE, indicating that the model generalizes better on the test set than anticipated by cross-validation.\n",
    "* Cross-Validation Scores (Negative MSE): These scores range from around -754 million to -324 billion, with a Mean CV RMSE of 341,713.84. The cross-validation errors are still high but slightly better than those for Employment_Rural.\n",
    "\n",
    "The Random Forest model performs well on Personal_Income_Rural, with high R² and manageable RMSE. The lower test set RMSE compared to the cross-validation RMSE suggests that the model is fairly robust and not overfitting, capturing complex relationships without excessive variance.\n",
    "\n",
    "3. **Target: Poverty_Levels**\n",
    "\n",
    "Test Set Performance:\n",
    "* R² Score: 0.94, meaning the model explains 94% of the variance in Poverty_Levels, which is lower than for the other targets but still strong.\n",
    "* RMSE: 139,691.87, which is lower than the Mean CV RMSE but indicates higher errors compared to the other targets.\n",
    "* Cross-Validation Scores (Negative MSE): The scores vary widely, from -4.3 billion to -527 billion, with a Mean CV RMSE of 430,534.24, showing higher variability and error.\n",
    "\n",
    "For Poverty_Levels, the Random Forest model has a slightly weaker fit with an R² of 0.94 and a high RMSE. While it performs reasonably well, the higher variability and cross-validation error suggest that the model might be struggling to capture all nuances of this target, potentially due to complexity or noise in the data.\n",
    "\n",
    "**Summary**\n",
    "\n",
    "For Poverty_Levels, the Random Forest model has a slightly weaker fit with an R² of 0.94 and a high RMSE. While it performs reasonably well, the higher variability and cross-validation error suggest that the model might be struggling to capture all nuances of this target, potentially due to complexity or noise in the data.\n",
    "\n",
    "#### Best Model: Random Forest\n",
    "\n",
    "1. **Employment: Actual vs Predicted (Left Plot)**\n",
    "\n",
    "* The predicted values (orange) are mostly close to the actual values (blue) and generally align with the ideal line (gray). This indicates that the model is performing well for Employment_Rural.\n",
    "* There are a few noticeable deviations where the predictions are slightly lower than the actual values, particularly at higher employment levels. However, the model captures the trend well, and these deviations are relatively minor.\n",
    "* The Random Forest model does a good job of predicting Employment_Rural, with only minor errors for larger values. The strong alignment with the ideal line supports the high R² score (0.97) and low RMSE reported.\n",
    "\n",
    "2. **Personal Income: Actual vs Predicted (Middle Plot)**\n",
    "\n",
    "* The predicted values (orange) generally follow the trend of the actual values (green) but display some larger deviations, especially as personal income increases.\n",
    "* There are a few outliers where the predicted values are noticeably lower than the actual values (e.g., at around 1,750,000). This suggests that the model may struggle to capture extreme values accurately, leading to slightly higher errors in those cases.\n",
    "* While the Random Forest model fits Personal_Income_Rural reasonably well, it shows a bit more error for high-income levels. However, the overall alignment with the ideal line supports the high R² (0.97) and suggests that the model is performing well for most data points.\n",
    "\n",
    "3. **Poverty Level: Actual vs Predicted (Right Plot)**\n",
    "\n",
    "* The predicted values (orange) generally follow the trend of the actual values (purple) but have more significant deviations, especially at higher poverty levels.\n",
    "* There are noticeable outliers, with the predicted values being either significantly higher or lower than the actual values. This is particularly evident at the highest values, where the model overestimates poverty levels.\n",
    "* The Random Forest model captures the general trend for Poverty_Levels but shows larger errors compared to the other targets. This aligns with the lower R² (0.94) and higher RMSE, indicating that the model has more difficulty accurately predicting Poverty_Levels, especially for higher values.\n",
    "\n",
    "**Overall Summary**\n",
    "\n",
    "* Good Fit for Employment and Personal Income: The model does well with Employment_Rural and Personal_Income_Rural, showing high alignment with the ideal line and only minor deviations. This supports the strong performance metrics (high R² and low RMSE) observed for these targets.\n",
    "* Challenges with Poverty Levels: The model shows greater deviations for Poverty_Levels, especially at higher values, suggesting some difficulty in capturing the full variability of this target. This could indicate that the model may benefit from further tuning or additional features to handle this complexity.\n",
    "\n",
    "In conclusion, the Random Forest model performs well overall, especially for Employment_Rural and Personal_Income_Rural, but has more difficulty accurately predicting Poverty_Levels at extreme values."
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