diff --git "a/wealthwavetransfer.ipynb" "b/wealthwavetransfer.ipynb"
new file mode 100644--- /dev/null
+++ "b/wealthwavetransfer.ipynb"
@@ -0,0 +1,593 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d69158bc",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-03T08:21:52.862165Z",
+     "iopub.status.busy": "2024-10-03T08:21:52.861695Z",
+     "iopub.status.idle": "2024-10-03T08:22:10.195100Z",
+     "shell.execute_reply": "2024-10-03T08:22:10.193405Z"
+    },
+    "papermill": {
+     "duration": 17.343622,
+     "end_time": "2024-10-03T08:22:10.198015",
+     "exception": false,
+     "start_time": "2024-10-03T08:21:52.854393",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: torch in /opt/conda/lib/python3.10/site-packages (2.4.0+cpu)\r\n",
+      "Requirement already satisfied: torchvision in /opt/conda/lib/python3.10/site-packages (0.19.0+cpu)\r\n",
+      "Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch) (3.15.1)\r\n",
+      "Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch) (4.12.2)\r\n",
+      "Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch) (1.12)\r\n",
+      "Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch) (3.3)\r\n",
+      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch) (3.1.4)\r\n",
+      "Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch) (2024.6.1)\r\n",
+      "Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision) (1.26.4)\r\n",
+      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision) (10.3.0)\r\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch) (2.1.5)\r\n",
+      "Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch) (1.3.0)\r\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install torch torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a5d17e9f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-03T08:22:10.211225Z",
+     "iopub.status.busy": "2024-10-03T08:22:10.210757Z",
+     "iopub.status.idle": "2024-10-03T08:22:14.038564Z",
+     "shell.execute_reply": "2024-10-03T08:22:14.037386Z"
+    },
+    "papermill": {
+     "duration": 3.838439,
+     "end_time": "2024-10-03T08:22:14.042030",
+     "exception": false,
+     "start_time": "2024-10-03T08:22:10.203591",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import torch\n",
+    "\n",
+    "# Generate synthetic data\n",
+    "np.random.seed(42)\n",
+    "num_samples = 1000\n",
+    "\n",
+    "# Features: Age, Income, Investments\n",
+    "age = np.random.randint(18, 70, size=num_samples)\n",
+    "income = np.random.normal(50000, 15000, size=num_samples)  # Average income\n",
+    "investments = np.random.normal(10000, 5000, size=num_samples)  # Average investments\n",
+    "\n",
+    "# Wealth target: a simple function of the features (you can modify this)\n",
+    "wealth = 0.4 * age + 0.5 * (income / 1000) + 0.3 * (investments / 1000) + np.random.normal(0, 5, size=num_samples)\n",
+    "\n",
+    "# Convert to PyTorch tensors\n",
+    "X = torch.tensor(np.column_stack((age, income, investments)), dtype=torch.float32)\n",
+    "y = torch.tensor(wealth, dtype=torch.float32).view(-1, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0ff2538a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-03T08:22:14.054123Z",
+     "iopub.status.busy": "2024-10-03T08:22:14.053562Z",
+     "iopub.status.idle": "2024-10-03T08:22:16.416550Z",
+     "shell.execute_reply": "2024-10-03T08:22:16.414693Z"
+    },
+    "papermill": {
+     "duration": 2.372429,
+     "end_time": "2024-10-03T08:22:16.419669",
+     "exception": false,
+     "start_time": "2024-10-03T08:22:14.047240",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch [10/100], Loss: 240105.7031\n",
+      "Epoch [20/100], Loss: 53837.3750\n",
+      "Epoch [30/100], Loss: 14999.7119\n",
+      "Epoch [40/100], Loss: 9553.0596\n",
+      "Epoch [50/100], Loss: 3178.5593\n",
+      "Epoch [60/100], Loss: 1612.6447\n",
+      "Epoch [70/100], Loss: 808.2083\n",
+      "Epoch [80/100], Loss: 502.7203\n",
+      "Epoch [90/100], Loss: 404.6421\n",
+      "Epoch [100/100], Loss: 366.8774\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "\n",
+    "class WealthModel(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(WealthModel, self).__init__()\n",
+    "        self.fc1 = nn.Linear(3, 64)  # 3 input features\n",
+    "        self.fc2 = nn.Linear(64, 32)\n",
+    "        self.fc3 = nn.Linear(32, 1)   # Output is a single value (wealth)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = torch.relu(self.fc1(x))\n",
+    "        x = torch.relu(self.fc2(x))\n",
+    "        x = self.fc3(x)  # No activation function on output layer for regression\n",
+    "        return x\n",
+    "\n",
+    "model = WealthModel()\n",
+    "\n",
+    "# Training settings\n",
+    "criterion = nn.MSELoss()\n",
+    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
+    "num_epochs = 100\n",
+    "\n",
+    "# Training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    model.train()\n",
+    "    \n",
+    "    # Forward pass\n",
+    "    outputs = model(X)\n",
+    "    loss = criterion(outputs, y)\n",
+    "    \n",
+    "    # Backward pass and optimization\n",
+    "    optimizer.zero_grad()\n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "    \n",
+    "    if (epoch+1) % 10 == 0:\n",
+    "        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "61b30e68",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-03T08:22:16.432570Z",
+     "iopub.status.busy": "2024-10-03T08:22:16.431981Z",
+     "iopub.status.idle": "2024-10-03T08:22:16.807617Z",
+     "shell.execute_reply": "2024-10-03T08:22:16.806170Z"
+    },
+    "papermill": {
+     "duration": 0.386,
+     "end_time": "2024-10-03T08:22:16.811415",
+     "exception": false,
+     "start_time": "2024-10-03T08:22:16.425415",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model.eval()\n",
+    "with torch.no_grad():\n",
+    "    predicted = model(X)\n",
+    "\n",
+    "# Optionally, you can visualize or calculate performance metrics\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.scatter(y.numpy(), predicted.numpy(), alpha=0.5)\n",
+    "plt.xlabel('True Wealth')\n",
+    "plt.ylabel('Predicted Wealth')\n",
+    "plt.title('True vs Predicted Wealth')\n",
+    "plt.plot([y.min(), y.max()], [y.min(), y.max()], '--', color='red')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "734b18f0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-03T08:22:16.826602Z",
+     "iopub.status.busy": "2024-10-03T08:22:16.826136Z",
+     "iopub.status.idle": "2024-10-03T08:22:16.833520Z",
+     "shell.execute_reply": "2024-10-03T08:22:16.832279Z"
+    },
+    "papermill": {
+     "duration": 0.017939,
+     "end_time": "2024-10-03T08:22:16.836109",
+     "exception": false,
+     "start_time": "2024-10-03T08:22:16.818170",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class ObfuscationLayer(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(ObfuscationLayer, self).__init__()\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # Add noise to simulate obfuscation/encryption\n",
+    "        noise = torch.normal(0, 0.1, x.size()).to(x.device)  # Adjust the standard deviation for noise level\n",
+    "        return x + noise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3302c028",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-03T08:22:16.851288Z",
+     "iopub.status.busy": "2024-10-03T08:22:16.850696Z",
+     "iopub.status.idle": "2024-10-03T08:22:16.862208Z",
+     "shell.execute_reply": "2024-10-03T08:22:16.860735Z"
+    },
+    "papermill": {
+     "duration": 0.022082,
+     "end_time": "2024-10-03T08:22:16.865131",
+     "exception": false,
+     "start_time": "2024-10-03T08:22:16.843049",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class EnhancedWealthModel(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(EnhancedWealthModel, self).__init__()\n",
+    "        self.obfuscation = ObfuscationLayer()\n",
+    "        self.fc1 = nn.Linear(3, 128)  # More units for complexity\n",
+    "        self.fc2 = nn.Linear(128, 64)\n",
+    "        self.fc3 = nn.Linear(64, 32)\n",
+    "        self.fc4 = nn.Linear(32, 1)    # Output is a single value (wealth)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = self.obfuscation(x)  # Apply obfuscation\n",
+    "        x = torch.relu(self.fc1(x))\n",
+    "        x = torch.relu(self.fc2(x))\n",
+    "        x = torch.relu(self.fc3(x))\n",
+    "        x = self.fc4(x)  # No activation function on output layer for regression\n",
+    "        return x\n",
+    "\n",
+    "model = EnhancedWealthModel()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7a148234",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-03T08:22:16.879415Z",
+     "iopub.status.busy": "2024-10-03T08:22:16.878937Z",
+     "iopub.status.idle": "2024-10-03T08:22:17.249237Z",
+     "shell.execute_reply": "2024-10-03T08:22:17.247517Z"
+    },
+    "papermill": {
+     "duration": 0.382133,
+     "end_time": "2024-10-03T08:22:17.253514",
+     "exception": false,
+     "start_time": "2024-10-03T08:22:16.871381",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch [10/100], Loss: 9025.9873\n",
+      "Epoch [20/100], Loss: 23535.0488\n",
+      "Epoch [30/100], Loss: 7839.4009\n",
+      "Epoch [40/100], Loss: 3846.0127\n",
+      "Epoch [50/100], Loss: 1887.0905\n",
+      "Epoch [60/100], Loss: 878.9637\n",
+      "Epoch [70/100], Loss: 470.7004\n",
+      "Epoch [80/100], Loss: 366.8524\n",
+      "Epoch [90/100], Loss: 278.9438\n",
+      "Epoch [100/100], Loss: 198.2601\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Training settings\n",
+    "criterion = nn.MSELoss()\n",
+    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
+    "num_epochs = 100\n",
+    "\n",
+    "# Training loop\n",
+    "for epoch in range(num_epochs):\n",
+    "    model.train()\n",
+    "    \n",
+    "    # Forward pass\n",
+    "    outputs = model(X)\n",
+    "    loss = criterion(outputs, y)\n",
+    "    \n",
+    "    # Backward pass and optimization\n",
+    "    optimizer.zero_grad()\n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "    \n",
+    "    if (epoch + 1) % 10 == 0:\n",
+    "        print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item():.4f}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7a8cf8ae",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-03T08:22:17.268933Z",
+     "iopub.status.busy": "2024-10-03T08:22:17.268481Z",
+     "iopub.status.idle": "2024-10-03T08:22:19.086909Z",
+     "shell.execute_reply": "2024-10-03T08:22:19.085349Z"
+    },
+    "papermill": {
+     "duration": 1.829363,
+     "end_time": "2024-10-03T08:22:19.089629",
+     "exception": false,
+     "start_time": "2024-10-03T08:22:17.260266",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 1800x600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# Define grid size\n",
+    "grid_size = 20\n",
+    "\n",
+    "# Generate a sine waveform to represent wealth data\n",
+    "def generate_wealth_waveform(grid_size):\n",
+    "    x = np.linspace(0, 2 * np.pi, grid_size)\n",
+    "    wealth_waveform = np.sin(x)\n",
+    "    return wealth_waveform\n",
+    "\n",
+    "# Create wealth data for the grid\n",
+    "wealth_waveform = generate_wealth_waveform(grid_size)\n",
+    "wealth_data = np.tile(wealth_waveform, (grid_size, 1))  # Repeat waveform along one axis\n",
+    "\n",
+    "# Convert wealth data to PyTorch tensor\n",
+    "wealth_data = torch.tensor(wealth_data, dtype=torch.float32)\n",
+    "\n",
+    "# Define a simple neural network to \"transfer\" wealth data to a targeted account\n",
+    "class WealthTransferNet(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(WealthTransferNet, self).__init__()\n",
+    "        self.fc1 = nn.Linear(grid_size * grid_size, 128)\n",
+    "        self.fc2 = nn.Linear(128, grid_size * grid_size)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = torch.relu(self.fc1(x))\n",
+    "        x = self.fc2(x)\n",
+    "        return x\n",
+    "\n",
+    "# Instantiate the network, loss function, and optimizer\n",
+    "net = WealthTransferNet()\n",
+    "criterion = nn.MSELoss()\n",
+    "optimizer = optim.Adam(net.parameters(), lr=0.01)\n",
+    "\n",
+    "# Target account: Wealth directed to bottom-right corner of the grid\n",
+    "target_account = torch.zeros((grid_size, grid_size))\n",
+    "target_account[-5:, -5:] = 1  # Simulating the transfer to a targeted account\n",
+    "\n",
+    "# Convert the grid to a single vector for the neural network\n",
+    "input_data = wealth_data.view(-1)\n",
+    "target_data = target_account.view(-1)\n",
+    "\n",
+    "# Training the network\n",
+    "epochs = 500\n",
+    "for epoch in range(epochs):\n",
+    "    optimizer.zero_grad()\n",
+    "    output = net(input_data)\n",
+    "    loss = criterion(output, target_data)\n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "\n",
+    "# Reshape the output to the grid size\n",
+    "output_grid = output.detach().view(grid_size, grid_size)\n",
+    "\n",
+    "# Plot the original wealth waveform and transferred wealth\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(18, 6))\n",
+    "axes[0].imshow(wealth_data, cmap='viridis')\n",
+    "axes[0].set_title('Original Wealth Waveform')\n",
+    "axes[1].imshow(target_account, cmap='viridis')\n",
+    "axes[1].set_title('Target Account Location')\n",
+    "axes[2].imshow(output_grid, cmap='viridis')\n",
+    "axes[2].set_title('Transferred Wealth to Target')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "76107e0d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-03T08:22:19.106067Z",
+     "iopub.status.busy": "2024-10-03T08:22:19.105611Z",
+     "iopub.status.idle": "2024-10-03T08:22:20.691712Z",
+     "shell.execute_reply": "2024-10-03T08:22:20.690211Z"
+    },
+    "papermill": {
+     "duration": 1.5976,
+     "end_time": "2024-10-03T08:22:20.694618",
+     "exception": false,
+     "start_time": "2024-10-03T08:22:19.097018",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# Define the size of the waveform\n",
+    "waveform_size = 100\n",
+    "\n",
+    "# Generate a sine waveform to represent wealth data\n",
+    "def generate_wealth_waveform(waveform_size):\n",
+    "    x = np.linspace(0, 2 * np.pi, waveform_size)\n",
+    "    wealth_waveform = np.sin(x)\n",
+    "    return wealth_waveform\n",
+    "\n",
+    "# Create wealth data as a single waveform\n",
+    "wealth_waveform = generate_wealth_waveform(waveform_size)\n",
+    "wealth_data = torch.tensor(wealth_waveform, dtype=torch.float32)\n",
+    "\n",
+    "# Define a neural network to transfer wealth data to a targeted point in the waveform\n",
+    "class WealthTransferNet(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(WealthTransferNet, self).__init__()\n",
+    "        self.fc1 = nn.Linear(waveform_size, 64)\n",
+    "        self.fc2 = nn.Linear(64, waveform_size)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = torch.relu(self.fc1(x))\n",
+    "        x = self.fc2(x)\n",
+    "        return x\n",
+    "\n",
+    "# Instantiate the network, loss function, and optimizer\n",
+    "net = WealthTransferNet()\n",
+    "criterion = nn.MSELoss()\n",
+    "optimizer = optim.Adam(net.parameters(), lr=0.01)\n",
+    "\n",
+    "# Target account: Wealth directed to the end of the waveform (right side)\n",
+    "target_account = torch.zeros(waveform_size)\n",
+    "target_account[-10:] = 1  # Simulating the transfer to the last 10 positions\n",
+    "\n",
+    "# Training the network\n",
+    "epochs = 1000\n",
+    "for epoch in range(epochs):\n",
+    "    optimizer.zero_grad()\n",
+    "    output = net(wealth_data)\n",
+    "    loss = criterion(output, target_account)\n",
+    "    loss.backward()\n",
+    "    optimizer.step()\n",
+    "\n",
+    "# Convert output to numpy for plotting\n",
+    "output_waveform = output.detach().numpy()\n",
+    "\n",
+    "# Plot the original and transferred wealth waveform\n",
+    "fig, ax = plt.subplots(figsize=(10, 5))\n",
+    "ax.plot(wealth_data.numpy(), label=\"Original Wealth Waveform\", linestyle=\"--\")\n",
+    "ax.plot(target_account.numpy(), label=\"Target Account\", linestyle=\":\")\n",
+    "ax.plot(output_waveform, label=\"Transferred Wealth Waveform\")\n",
+    "ax.set_title('WealthWaveTransfer')\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kaggle": {
+   "accelerator": "none",
+   "dataSources": [],
+   "dockerImageVersionId": 30775,
+   "isGpuEnabled": false,
+   "isInternetEnabled": true,
+   "language": "python",
+   "sourceType": "notebook"
+  },
+  "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": "3.10.14"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": 32.458252,
+   "end_time": "2024-10-03T08:22:21.927398",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "__notebook__.ipynb",
+   "output_path": "__notebook__.ipynb",
+   "parameters": {},
+   "start_time": "2024-10-03T08:21:49.469146",
+   "version": "2.6.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}