KERAS AND TENSOR FLOW IN LINEAR REGRESSION.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "770d34b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/100, Loss: 2.249438524246216\n",
      "Epoch 20/100, Loss: 1.3901599645614624\n",
      "Epoch 30/100, Loss: 0.8694143891334534\n",
      "Epoch 40/100, Loss: 0.553587794303894\n",
      "Epoch 50/100, Loss: 0.36180824041366577\n",
      "Epoch 60/100, Loss: 0.24512650072574615\n",
      "Epoch 70/100, Loss: 0.1739148646593094\n",
      "Epoch 80/100, Loss: 0.1302402764558792\n",
      "Epoch 90/100, Loss: 0.10324779152870178\n",
      "Epoch 100/100, Loss: 0.08636733889579773\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate synthetic data\n",
    "np.random.seed(0)\n",
    "X = np.random.rand(100, 1).astype(np.float32)  # Cast X to float32\n",
    "y = 2 * X + 1 + np.random.randn(100, 1) * 0.1  # Linear relationship with noise\n",
    "\n",
    "# Define the model\n",
    "class LinearRegression(tf.Module):\n",
    "    def __init__(self):\n",
    "        super(LinearRegression, self).__init__()\n",
    "        self.W = tf.Variable(tf.random.normal([1, 1]), name='weight')\n",
    "        self.b = tf.Variable(tf.zeros([1]), name='bias')\n",
    "\n",
    "    def __call__(self, x):\n",
    "        return tf.matmul(x, self.W) + self.b\n",
    "\n",
    "# Define loss function\n",
    "def loss(y_true, y_pred):\n",
    "    return tf.reduce_mean(tf.square(y_true - y_pred))\n",
    "\n",
    "# Define optimization algorithm\n",
    "optimizer = tf.optimizers.SGD(learning_rate=0.01)\n",
    "\n",
    "# Instantiate the model\n",
    "model = LinearRegression()\n",
    "\n",
    "# Training loop\n",
    "epochs = 100\n",
    "for epoch in range(epochs):\n",
    "    with tf.GradientTape() as tape:\n",
    "        predictions = model(X)\n",
    "        current_loss = loss(y, predictions)\n",
    "    gradients = tape.gradient(current_loss, model.trainable_variables)\n",
    "    optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
    "    \n",
    "    if (epoch + 1) % 10 == 0:\n",
    "        print(f'Epoch {epoch+1}/{epochs}, Loss: {current_loss.numpy()}')\n",
    "\n",
    "# Plot the data and the learned line\n",
    "plt.scatter(X, y)\n",
    "plt.plot(X, model(X), color='red')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('y')\n",
    "plt.title('Linear Regression using TensorFlow')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c0ed84ba",
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "\n`load_boston` has been removed from scikit-learn since version 1.2.\n\nThe Boston housing prices dataset has an ethical problem: as\ninvestigated in [1], the authors of this dataset engineered a\nnon-invertible variable \"B\" assuming that racial self-segregation had a\npositive impact on house prices [2]. Furthermore the goal of the\nresearch that led to the creation of this dataset was to study the\nimpact of air quality but it did not give adequate demonstration of the\nvalidity of this assumption.\n\nThe scikit-learn maintainers therefore strongly discourage the use of\nthis dataset unless the purpose of the code is to study and educate\nabout ethical issues in data science and machine learning.\n\nIn this special case, you can fetch the dataset from the original\nsource::\n\n    import pandas as pd\n    import numpy as np\n\n    data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n    raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n    data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n    target = raw_df.values[1::2, 2]\n\nAlternative datasets include the California housing dataset and the\nAmes housing dataset. You can load the datasets as follows::\n\n    from sklearn.datasets import fetch_california_housing\n    housing = fetch_california_housing()\n\nfor the California housing dataset and::\n\n    from sklearn.datasets import fetch_openml\n    housing = fetch_openml(name=\"house_prices\", as_frame=True)\n\nfor the Ames housing dataset.\n\n[1] M Carlisle.\n\"Racist data destruction?\"\n<https://medium.com/@docintangible/racist-data-destruction-113e3eff54a8>\n\n[2] Harrison Jr, David, and Daniel L. Rubinfeld.\n\"Hedonic housing prices and the demand for clean air.\"\nJournal of environmental economics and management 5.1 (1978): 81-102.\n<https://www.researchgate.net/publication/4974606_Hedonic_housing_prices_and_the_demand_for_clean_air>\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[3], line 4\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdatasets\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m load_boston\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_selection\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m train_test_split\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpreprocessing\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m StandardScaler\n",
      "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\datasets\\__init__.py:156\u001b[0m, in \u001b[0;36m__getattr__\u001b[1;34m(name)\u001b[0m\n\u001b[0;32m    105\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mload_boston\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m    106\u001b[0m     msg \u001b[38;5;241m=\u001b[39m textwrap\u001b[38;5;241m.\u001b[39mdedent(\n\u001b[0;32m    107\u001b[0m \u001b[38;5;250m        \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    108\u001b[0m \u001b[38;5;124;03m        `load_boston` has been removed from scikit-learn since version 1.2.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    154\u001b[0m \u001b[38;5;124;03m        \"\"\"\u001b[39;00m\n\u001b[0;32m    155\u001b[0m     )\n\u001b[1;32m--> 156\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(msg)\n\u001b[0;32m    157\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    158\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mglobals\u001b[39m()[name]\n",
      "\u001b[1;31mImportError\u001b[0m: \n`load_boston` has been removed from scikit-learn since version 1.2.\n\nThe Boston housing prices dataset has an ethical problem: as\ninvestigated in [1], the authors of this dataset engineered a\nnon-invertible variable \"B\" assuming that racial self-segregation had a\npositive impact on house prices [2]. Furthermore the goal of the\nresearch that led to the creation of this dataset was to study the\nimpact of air quality but it did not give adequate demonstration of the\nvalidity of this assumption.\n\nThe scikit-learn maintainers therefore strongly discourage the use of\nthis dataset unless the purpose of the code is to study and educate\nabout ethical issues in data science and machine learning.\n\nIn this special case, you can fetch the dataset from the original\nsource::\n\n    import pandas as pd\n    import numpy as np\n\n    data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n    raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n    data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n    target = raw_df.values[1::2, 2]\n\nAlternative datasets include the California housing dataset and the\nAmes housing dataset. You can load the datasets as follows::\n\n    from sklearn.datasets import fetch_california_housing\n    housing = fetch_california_housing()\n\nfor the California housing dataset and::\n\n    from sklearn.datasets import fetch_openml\n    housing = fetch_openml(name=\"house_prices\", as_frame=True)\n\nfor the Ames housing dataset.\n\n[1] M Carlisle.\n\"Racist data destruction?\"\n<https://medium.com/@docintangible/racist-data-destruction-113e3eff54a8>\n\n[2] Harrison Jr, David, and Daniel L. Rubinfeld.\n\"Hedonic housing prices and the demand for clean air.\"\nJournal of environmental economics and management 5.1 (1978): 81-102.\n<https://www.researchgate.net/publication/4974606_Hedonic_housing_prices_and_the_demand_for_clean_air>\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_boston\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Load the Boston Housing dataset\n",
    "boston = load_boston()\n",
    "X, y = boston.data, boston.target\n",
    "\n",
    "# Standardize the features\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "\n",
    "# Split the data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Define the model\n",
    "class LinearRegression(tf.Module):\n",
    "    def __init__(self):\n",
    "        super(LinearRegression, self).__init__()\n",
    "        self.W = tf.Variable(tf.random.normal([X.shape[1], 1]), name='weight')\n",
    "        self.b = tf.Variable(tf.zeros([1]), name='bias')\n",
    "\n",
    "    def __call__(self, x):\n",
    "        return tf.matmul(x, self.W) + self.b\n",
    "\n",
    "# Define loss function\n",
    "def loss(y_true, y_pred):\n",
    "    return tf.reduce_mean(tf.square(y_true - y_pred))\n",
    "\n",
    "# Define optimization algorithm\n",
    "optimizer = tf.optimizers.Adam(learning_rate=0.01)  # Using Adam optimizer\n",
    "\n",
    "# Instantiate the model\n",
    "model = LinearRegression()\n",
    "\n",
    "# Training loop\n",
    "epochs = 100\n",
    "batch_size = 32\n",
    "num_batches = len(X_train) // batch_size\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    for i in range(num_batches):\n",
    "        start = i * batch_size\n",
    "        end = (i + 1) * batch_size\n",
    "        X_batch = X_train[start:end]\n",
    "        y_batch = y_train[start:end]\n",
    "        \n",
    "        with tf.GradientTape() as tape:\n",
    "            predictions = model(X_batch)\n",
    "            current_loss = loss(y_batch, predictions)\n",
    "        gradients = tape.gradient(current_loss, model.trainable_variables)\n",
    "        optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
    "    \n",
    "    if (epoch + 1) % 10 == 0:\n",
    "        print(f'Epoch {epoch+1}/{epochs}, Loss: {current_loss.numpy()}')\n",
    "\n",
    "# Evaluate the model\n",
    "test_predictions = model(X_test)\n",
    "test_loss = loss(y_test, test_predictions)\n",
    "print(f'Test Loss: {test_loss.numpy()}')\n",
    "\n",
    "# Plot predicted vs actual values for test set\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(y_test, test_predictions, alpha=0.5)\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.title('Predicted vs Actual Values')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c35db42c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "413/413 [==============================] - 2s 2ms/step - loss: 5.9983 - val_loss: 4.5893\n",
      "Epoch 2/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 3.8198 - val_loss: 2.9895\n",
      "Epoch 3/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 2.5479 - val_loss: 2.0233\n",
      "Epoch 4/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 1.7698 - val_loss: 1.4334\n",
      "Epoch 5/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 1.2947 - val_loss: 1.0818\n",
      "Epoch 6/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 1.0158 - val_loss: 0.8804\n",
      "Epoch 7/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.8534 - val_loss: 0.7672\n",
      "Epoch 8/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.7605 - val_loss: 0.7043\n",
      "Epoch 9/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.7040 - val_loss: 0.6658\n",
      "Epoch 10/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.6660 - val_loss: 0.6393\n",
      "Epoch 11/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.6366 - val_loss: 0.6186\n",
      "Epoch 12/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.6121 - val_loss: 0.6019\n",
      "Epoch 13/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5912 - val_loss: 0.5890\n",
      "Epoch 14/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5745 - val_loss: 0.5781\n",
      "Epoch 15/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5608 - val_loss: 0.5700\n",
      "Epoch 16/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5498 - val_loss: 0.5633\n",
      "Epoch 17/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5409 - val_loss: 0.5591\n",
      "Epoch 18/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5337 - val_loss: 0.5551\n",
      "Epoch 19/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5278 - val_loss: 0.5534\n",
      "Epoch 20/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5240 - val_loss: 0.5509\n",
      "Epoch 21/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5204 - val_loss: 0.5492\n",
      "Epoch 22/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5182 - val_loss: 0.5488\n",
      "Epoch 23/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5163 - val_loss: 0.5470\n",
      "Epoch 24/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5148 - val_loss: 0.5470\n",
      "Epoch 25/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5143 - val_loss: 0.5466\n",
      "Epoch 26/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5133 - val_loss: 0.5464\n",
      "Epoch 27/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5132 - val_loss: 0.5459\n",
      "Epoch 28/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5129 - val_loss: 0.5450\n",
      "Epoch 29/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5130 - val_loss: 0.5451\n",
      "Epoch 30/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5445\n",
      "Epoch 31/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5129 - val_loss: 0.5451\n",
      "Epoch 32/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5129 - val_loss: 0.5451\n",
      "Epoch 33/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5123 - val_loss: 0.5447\n",
      "Epoch 34/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5453\n",
      "Epoch 35/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5128 - val_loss: 0.5446\n",
      "Epoch 36/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5126 - val_loss: 0.5446\n",
      "Epoch 37/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5120 - val_loss: 0.5466\n",
      "Epoch 38/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5442\n",
      "Epoch 39/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5128 - val_loss: 0.5454\n",
      "Epoch 40/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5449\n",
      "Epoch 41/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5457\n",
      "Epoch 42/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5127 - val_loss: 0.5447\n",
      "Epoch 43/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5122 - val_loss: 0.5461\n",
      "Epoch 44/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5451\n",
      "Epoch 45/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5123 - val_loss: 0.5462\n",
      "Epoch 46/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5123 - val_loss: 0.5469\n",
      "Epoch 47/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5470\n",
      "Epoch 48/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5463\n",
      "Epoch 49/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5123 - val_loss: 0.5466\n",
      "Epoch 50/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5123 - val_loss: 0.5454\n",
      "Epoch 51/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5447\n",
      "Epoch 52/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5451\n",
      "Epoch 53/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5452\n",
      "Epoch 54/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5122 - val_loss: 0.5466\n",
      "Epoch 55/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5123 - val_loss: 0.5470\n",
      "Epoch 56/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5465\n",
      "Epoch 57/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5123 - val_loss: 0.5467\n",
      "Epoch 58/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5138 - val_loss: 0.5451\n",
      "Epoch 59/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5121 - val_loss: 0.5472\n",
      "Epoch 60/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5122 - val_loss: 0.5458\n",
      "Epoch 61/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5462\n",
      "Epoch 62/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5121 - val_loss: 0.5463\n",
      "Epoch 63/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5123 - val_loss: 0.5470\n",
      "Epoch 64/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5459\n",
      "Epoch 65/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5122 - val_loss: 0.5455\n",
      "Epoch 66/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5128 - val_loss: 0.5454\n",
      "Epoch 67/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5127 - val_loss: 0.5458\n",
      "Epoch 68/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5126 - val_loss: 0.5465\n",
      "Epoch 69/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5459\n",
      "Epoch 70/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5126 - val_loss: 0.5459\n",
      "Epoch 71/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5123 - val_loss: 0.5451\n",
      "Epoch 72/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5123 - val_loss: 0.5459\n",
      "Epoch 73/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5472\n",
      "Epoch 74/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5122 - val_loss: 0.5452\n",
      "Epoch 75/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5121 - val_loss: 0.5461\n",
      "Epoch 76/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5129 - val_loss: 0.5447\n",
      "Epoch 77/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5126 - val_loss: 0.5460\n",
      "Epoch 78/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5470\n",
      "Epoch 79/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5123 - val_loss: 0.5462\n",
      "Epoch 80/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5457\n",
      "Epoch 81/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5127 - val_loss: 0.5460\n",
      "Epoch 82/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5122 - val_loss: 0.5460\n",
      "Epoch 83/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5472\n",
      "Epoch 84/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5126 - val_loss: 0.5453\n",
      "Epoch 85/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5451\n",
      "Epoch 86/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5121 - val_loss: 0.5446\n",
      "Epoch 87/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5123 - val_loss: 0.5462\n",
      "Epoch 88/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5455\n",
      "Epoch 89/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5128 - val_loss: 0.5464\n",
      "Epoch 90/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5122 - val_loss: 0.5477\n",
      "Epoch 91/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5127 - val_loss: 0.5461\n",
      "Epoch 92/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5126 - val_loss: 0.5449\n",
      "Epoch 93/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5121 - val_loss: 0.5472\n",
      "Epoch 94/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5457\n",
      "Epoch 95/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5128 - val_loss: 0.5469\n",
      "Epoch 96/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5460\n",
      "Epoch 97/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5126 - val_loss: 0.5450\n",
      "Epoch 98/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5451\n",
      "Epoch 99/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5122 - val_loss: 0.5453\n",
      "Epoch 100/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5452\n",
      "129/129 [==============================] - 0s 2ms/step - loss: 0.5592\n",
      "Test Loss: 0.5592385530471802\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Load the dataset\n",
    "housing = fetch_california_housing()\n",
    "\n",
    "# Split the dataset into features (X) and target (y)\n",
    "X = housing.data\n",
    "y = housing.target.reshape(-1, 1)  # Reshape to make it a column vector\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Standardize features\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# Define the model\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Input(shape=(X_train.shape[1],)),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(X_train, y_train, epochs=100, batch_size=32, validation_split=0.2)\n",
    "\n",
    "# Evaluate the model\n",
    "loss = model.evaluate(X_test, y_test)\n",
    "print(\"Test Loss:\", loss)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "152a5650",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "413/413 [==============================] - 2s 3ms/step - loss: 5.0103 - val_loss: 3.8850\n",
      "Epoch 2/10\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 3.1264 - val_loss: 2.5131\n",
      "Epoch 3/10\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 2.0471 - val_loss: 1.6836\n",
      "Epoch 4/10\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 1.3788 - val_loss: 1.1666\n",
      "Epoch 5/10\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.9712 - val_loss: 0.8595\n",
      "Epoch 6/10\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.7390 - val_loss: 0.6924\n",
      "Epoch 7/10\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.6179 - val_loss: 0.6102\n",
      "Epoch 8/10\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5618 - val_loss: 0.5745\n",
      "Epoch 9/10\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5381 - val_loss: 0.5611\n",
      "Epoch 10/10\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5282 - val_loss: 0.5553\n",
      "129/129 [==============================] - 0s 2ms/step - loss: 0.5656\n",
      "Test Loss: 0.5655983090400696\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "129/129 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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6Tp48iR9++MGw2m3w4MFISkpCu3btDMvF586di5iYmBqrrYjs5uoMZCKluOeeewQ/Pz/h8uXLZtsMHz5c8PHxEbKzswVBEITz588LY8eOFSIiIgRfX18hKipKGDZsmHDp0iXDfVauXCk0a9ZM8PX1FQAIM2bMMPzuyy+/FBISEgSNRiMkJiYK33zzjcnVTEuWLBFuu+02Qa1WC02aNBHmzJkjLF68WAAgZGZmGtqJWc1U1ciRIwUAQpcuXWr8bsOGDUL//v2Fhg0bCn5+fkJ4eLgwYMAA4ffffzdqB0DUY+pXM1kTExMjDBw40OTvrly5IkycOFGIi4sTfH19hdDQUKFt27bCSy+9JNy4ccPQ7u+//xbuu+8+oU6dOkJQUJBw3333Cbt377a6mklv2bJlQvv27QWNRiPUqVNHuP32243ul5ubK9x///1CSEiIoFKpjI5RVlYmvPvuu0KrVq0M92/WrJnw+OOPCydOnDC0KykpEZ577jkhPDxc0Gg0QqdOnYSUlBQhJiZG9GqmCRMmWGyjX7H03Xffmf1d1dVMgiAIKSkpQv/+/QWtViuo1WohPj6+xqqrzMxMYezYsULDhg0FX19foX79+kLnzp2F2bNnG9q89957QufOnYV69eoJfn5+QuPGjYVHH31UOHPmjNXnRiSVShBsqLpFREREpBBczURERERujcEMERERuTUGM0REROTWGMwQERGRW2MwQ0RERG6NwQwRERG5NY8vmqfT6XDx4kUEBQWJKulNREREricIAq5fv46oqCh4eVkee/H4YObixYs27bFCRERErnf+/Hmrm796fDCj39jt/PnzLi0TTkREROIVFBQgOjracB23xOODGf3UUnBwMIMZIiIiNyMmRYQJwEREROTWGMwQERGRW2MwQ0RERG6NwQwRERG5NQYzRERE5NYYzBAREZFbYzBDREREbo3BDBEREbk1BjNERETk1jy+AjCRq1ToBKRm5uLy9WKEB2nQIS4U3l7c7JSISG4MZogcYFN6Fmb9kIGs/GLDbZFaDWYMTkS/pEgX9oyIyPNwmolIZpvSs/Dk8oNGgQwAZOcX48nlB7EpPctFPSMi8kwMZohkVKETMOuHDAgmfqe/bdYPGajQmWpBRES2YDBDJKPUzNwaIzJVCQCy8ouRmpnrvE4REXk4BjNEMrp83XwgY0s7IiKyjsEMkYzCgzSytiMiIusYzBDJqENcKCK1GphbgK1C5aqmDnGhzuwWEZFHYzBDJCNvLxVmDE4EgBoBjf7nGYMTWW+GiEhGDGaIZNYvKRILR7dBhNZ4KilCq8HC0W1YZ4aISGYsmkfkAP2SItEnMYIVgImInIDBDJGDeHupkBwf5upuEBF5PE4zERERkVtjMENERERujcEMERERuTUGM0REROTWGMwQERGRW2MwQ0RERG6NwQwRERG5NQYzRERE5NYYzBAREZFbYzBDREREbo3BDBEREbk1BjNERETk1lwazOzYsQODBw9GVFQUVCoV1q5da/R7QRAwc+ZMREVFwd/fHz169MCxY8dc01kiIiJSJJcGM4WFhWjVqhXmz59v8vdvv/023n//fcyfPx/79u1DREQE+vTpg+vXrzu5p0RERKRUPq588P79+6N///4mfycIAubOnYuXXnoJQ4cOBQB8+eWXaNCgAVasWIHHH3/cmV0lIiIihVJszkxmZiays7PRt29fw21qtRrdu3fH7t27zd6vpKQEBQUFRv+IiIjIcyk2mMnOzgYANGjQwOj2Bg0aGH5nypw5c6DVag3/oqOjHdpPIiIici3FBjN6KpXK6GdBEGrcVtXUqVORn59v+Hf+/HlHd5GIiIhcyKU5M5ZEREQAqByhiYyMNNx++fLlGqM1VanVaqjVaof3j4iIiJRBsSMzcXFxiIiIwJYtWwy3lZaWYvv27ejcubMLe0ZERERK4tKRmRs3buDkyZOGnzMzM5GWlobQ0FA0btwYkyZNwhtvvIGmTZuiadOmeOONNxAQEICRI0e6sNdERESkJC4NZvbv34+ePXsafp48eTIAYMyYMfjiiy/wwgsv4ObNmxg/fjzy8vLQsWNH/PzzzwgKCnJVl4mIiEhhVIIgCK7uhCMVFBRAq9UiPz8fwcHBru4OERERiSDl+q3YnBkiIiIiMRjMEBERkVtjMENERERujcEMERERuTUGM0REROTWGMwQERGRW2MwQ0RERG6NwQwRERG5NQYzRERE5NYYzBAREZFbYzBDREREbo3BDBEREbk1BjNERETk1hjMEBERkVtjMENERERujcEMERERuTUGM0REROTWGMwQERGRW2MwQ0RERG6NwQwRERG5NQYzRERE5NYYzBAREZFbYzBDREREbo3BDBEREbk1BjNERETk1hjMEBERkVtjMENERERujcEMERERuTUGM0REROTWGMwQERGRW2MwQ0RERG6NwQwRERG5NQYzRERE5NYYzBAREZFbYzBDREREbo3BDBEREbk1BjNERETk1hjMEBERkVtjMENERERujcEMERERuTUGM0REROTWGMwQERGRW2MwQ0RERG6NwQwRERG5NQYzRERE5NYYzBAREZFbYzBDREREbo3BDBEREbk1BjNERETk1hjMEBERkVtTdDBTXl6Ol19+GXFxcfD390eTJk3w6quvQqfTubprREREpBA+ru6AJW+99RYWLVqEL7/8Es2bN8f+/fvxyCOPQKvV4plnnnF194iIiEgBFB3MpKSkYMiQIRg4cCAAIDY2FitXrsT+/ftd3DMiIiJSCkVPM91xxx3YunUr/vrrLwDA4cOHsXPnTgwYMMDsfUpKSlBQUGD0j4iIiDyXokdmXnzxReTn56NZs2bw9vZGRUUFXn/9dYwYMcLsfebMmYNZs2Y5sZdERETkSooemfnmm2+wfPlyrFixAgcPHsSXX36Jd999F19++aXZ+0ydOhX5+fmGf+fPn3dij4mIiMjZVIIgCK7uhDnR0dGYMmUKJkyYYLht9uzZWL58Of744w9RxygoKIBWq0V+fj6Cg4Md1VUiIiKSkZTrt6JHZoqKiuDlZdxFb29vLs0mIiIiA0XnzAwePBivv/46GjdujObNm+PQoUN4//33MXbsWFd3jYiIiBRC0dNM169fx/Tp07FmzRpcvnwZUVFRGDFiBF555RX4+fmJOganmYiIiNyPlOu3ooMZOTCYISIicj8ekzNDREREZA2DGSIiInJrDGaIiIjIrTGYISIiIrfGYIaIiIjcGoMZIiIicmsMZoiIiMitKboCMBERESlThU5AamYuLl8vRniQBh3iQuHtpXJJXxjMEBERkSSb0rMw64cMZOUXG26L1GowY3Ai+iVFOr0/nGYiIiIi0TalZ+HJ5QeNAhkAyM4vxpPLD2JTepbT+8RghoiIiESp0AmY9UMGTO2DpL9t1g8ZqNA5d6ckBjNEREQkSmpmbo0RmaoEAFn5xUjNzHVep8BghoiIiES6fN18IGNLO7kwmCEiIiJRwoM0sraTC4MZIiIiEqVDXCgitRqYW4CtQuWqpg5xoc7sFoMZIiIiEsfbS4UZgxNNJgADlTkzMwYnOr3eDIMZIiIiEu37g3/b9XtHYDBDREREotwsrcCWjMsW22zJuIybpRVO6lElBjNEREQkyhs/ZcjaTi4MZoiIiEiUMzlFsraTC4MZIiIiEqVRiL+s7eTCYIaIiIhEEaCTtZ1cGMwQERGRKBeulcjaTi52BzMFBQVYu3Ytjh8/Lkd/iIiISKFiwwJkbScXycHMsGHDMH/+fADAzZs30a5dOwwbNgwtW7bE999/L3sHiYiISBme79tM1nZykRzM7NixA127dgUArFmzBoIg4Nq1a5g3bx5mz54teweJiIhIGb7df17WdnKRHMzk5+cjNLRyz4VNmzbhvvvuQ0BAAAYOHIgTJ07I3kEiIiJShn1ncmVtJxfJwUx0dDRSUlJQWFiITZs2oW/fvgCAvLw8aDTO3SWTiIiInCfAz1vWdnKRHMxMmjQJo0aNQqNGjRAZGYkePXoAqJx+atGihdz9IyIiIoW47/ZGsraTi4/UO4wfPx4dOnTA+fPn0adPH3h5VcZDTZo0Yc4MERGRB+vctB4C/LxRZGHvpQA/b3RuWs+JvbJxaXa7du0wcOBAXLhwAeXl5QCAgQMHokuXLrJ2joiIiJTD20uFBzs1ttjmwU6N4e2lclKPKkkOZoqKivDoo48iICAAzZs3x7lz5wAAEydOxJtvvil7B4mIiEgZKnQC1h/Osthm/eEsVOgEJ/WokuRgZurUqTh8+DC2bdtmlPDbu3dvfPPNN7J2joiIiJQjNTMXWfnFFttk5RcjNdO5q5kk58ysXbsW33zzDTp16gSV6t9hpMTERJw6dUrWzhEREZFyXL5uOZCR2k4ukkdmrly5gvDw8Bq3FxYWGgU3RERE5FnCg8SVYBHbTi6Sg5n27dvjxx9/NPysD2A+++wzJCcny9czIiIiUpQOcaGI1FoOVCK1GnSIC3VSjypJnmaaM2cO+vXrh4yMDJSXl+PDDz/EsWPHkJKSgu3btzuij0RERKQA3l4q3N0qEp/syDTb5u5WkcpfzdS5c2fs2rULRUVFiI+Px88//4wGDRogJSUFbdu2dUQfiYiISAGUuppJ8sgMALRo0QJffvml3H0hIiIiBZOymik5PsxJvbJhZOann37C5s2ba9y+efNmbNy4UZZOERERkfJ4zGqmKVOmoKKiZhljQRAwZcoUWTpFREREyuMxq5lOnDiBxMTEGrc3a9YMJ0+elKVTREREpDz61Uzm0ntVcM1qJsnBjFarxenTp2vcfvLkSQQGBsrSKSIiIlIeby8VZgyuHNCoHtDof54xOFH5q5nuvvtuTJo0yaja78mTJ/Hcc8/h7rvvlrVzREREpCz9kiLxWLc4VK+Tq1IBj3WLQ7+kSKf3SXIw88477yAwMBDNmjVDXFwc4uLikJCQgLCwMLz77ruO6CMREREpxKb0LHy6IxPVV1/rBODTHZnYlG556bYjSF6ardVqsXv3bmzZsgWHDx+Gv78/WrZsiW7dujmif0RERKQQFToBs37IgKUqMrN+yECfxAinTjXZVGdGpVKhb9++6Nu3r9z9ISIiIoWyVmdGgGvqzIgKZubNm4fHHnsMGo0G8+bNs9h24sSJsnSMiIiIlEWpdWZEBTMffPABRo0aBY1Ggw8++MBsO5VKxWCGiIjIQym1zoyoYCYzM9Pk/4mIiKj26BAXipAAX1wrKjPbJiTAV9l1ZsrKytCkSRNkZGQ4qj81XLhwAaNHj0ZYWBgCAgLQunVrHDhwwGmPT0REROI5t8JMJUkJwL6+vigpKYGq+uJyB8nLy0OXLl3Qs2dPbNy4EeHh4Th16hRCQkKc8vhERET0r9TMXIujMgCQV1SmzATgqp5++mm89dZb+Pzzz+HjY9NiKNHeeustREdHY+nSpYbbYmNjHfqYREREZFp2/k1Z28lFcjSyd+9ebN26FT///DNatGhRYwuD1atXy9a59evX46677sJ//vMfbN++HQ0bNsT48eMxbtw4s/cpKSlBSUmJ4eeCggLZ+kNERFSb5RaWytpOLpKDmZCQENx3332O6EsNp0+fxsKFCzF58mRMmzYNqampmDhxItRqNR566CGT95kzZw5mzZrllP4RERHVJqF11LK2k4tKEARLhfxcys/PD+3atcPu3bsNt02cOBH79u1DSkqKyfuYGpmJjo5Gfn4+goODHd5nIiJyPxU6AamZubh8vRjhQZW7Pjt7s0RbObPvu05cxajFe622+/rRjujStJ5dj1VQUACtVivq+i16ZEan0+G9997D2rVrUVZWht69e+OVV16BRuO4teSRkZFITEw0ui0hIQHff/+92fuo1Wqo1c6NCImIyH1tSs/CrB8yjCrbRmo1mDE40SWbJkrh7L7rRI5/iG0nF9FLs9966y1MmTIFgYGBiIyMxPvvv+/wAnldunTBn3/+aXTbX3/9hZiYGIc+LhER1Q6b0rPw5PKDNUr0Z+cX48nlB12yaaJYruj73sxcWdvJRXQw88UXX+Cjjz7Czz//jHXr1mHt2rVYtmwZHDlL9eyzz2LPnj144403cPLkSaxYsQKffvopJkyY4LDHJCKi2sHSpon622b9kIGK6ttDK4Dr+i72eAodmTl79iwGDRpk+Pmuu+6CIAi4ePGiQzoGAO3bt8eaNWuwcuVKJCUl4bXXXsPcuXMxatQohz0mERHVDlI2TVQaV/U9uYm4PBix7eQiOmemtLQU/v7+hp9VKhX8/PyMkm0dYdCgQUZBFBERkRyUummiGK7qe6f4MAT4eaOotMJsm0A/b3RyYsE8QOLS7OnTpyMgIMDwc2lpKV5//XVotVrDbe+//758vSMiInIQpW6aKIaS++6KSTnRwUy3bt1qJON27twZp0+fNvzsrG0OiIiI7NUhLhSRWg2y84tNXoBVACK0GqdvmiiGq/q+51SOxVEZACgqrcCeUzl2L82WQnQws23bNgd2g4iIyLm8vVSYMTgRTy4/CBWMRxT0X81nDE5UZL0ZV/U95fRV0e2cGcxI2jWbiIjIk/RLisTC0W0QoTWejonQarBwdBtF15lxRd/Fro5y9gowx+4USUREpHD9kiLRJzHCLSsAO7vv+Tct75gttZ1cGMwQEVGt5+2lQrKTV+DIxZl9zy4QtzpKbDu5cJqJiIiIRMm8UihrO7kwmCEiIiJRAtXesraTi6hppiNHjog+YMuWLW3uDBERESlXq+gQpF+8LqqdM4kKZlq3bg2VSgVBEKzWkqmosLz+nIiIiNzT1P6J+HrveVHtnEnUNFNmZiZOnz6NzMxMfP/994iLi8OCBQtw6NAhHDp0CAsWLEB8fDy+//57R/eXiIiIXOTQuTxZ28lF1MhMTEyM4f//+c9/MG/ePAwYMMBwW8uWLREdHY3p06fjnnvukb2TRERE5HrfH/hbdLuut9Z3cG/+JXlp9tGjRxEXF1fj9ri4OGRkZMjSKSIico0KneCW9VbIOc7niVulJLadXCQHMwkJCZg9ezYWL14Mjaay6mBJSQlmz56NhIQE2TtIRETOsSk9C7N+yEBW/r81QiK1GswYnKjoSrjkPBpfcWGD2HZykfxoixYtwuDBgxEdHY1WrVoBAA4fPgyVSoUNGzbI3kEiInK8TelZeHL5wRqbFmbnF+PJ5QcVX9qfnKNVoxDsOpUjqp0zSQ5mOnTogMzMTCxfvhx//PEHBEHAAw88gJEjRyIwMNARfSQiO3HqgCyp0AmY9UOGyd2XBVRuXDjrhwz0SYzg+6aW69K0HhZsPyWqnTPZNA4UEBCAxx57TO6+EJEDcOqArEnNzDV6f1QnAMjKL0ZqZq7blvwnebSPDYVKBQgW9pFUqSrbOZNNFYC/+uor3HHHHYiKisLZs2cBAB988AHWrVsna+eIyD76qYPqFyr91MGm9CwX9YyU5PJ1cfvoiG1HNVXoBKScysG6tAtIOZXj1F2l5XzsA2fzLAYyQGWgc+CsApdmV7Vw4UK88sormDRpEmbPnm0okle3bl3MnTsXQ4YMkb2TRCQdpw5IrPAgjaztyJgrR0flfmylBr6SR2Y++ugjfPbZZ3jppZfg4/NvLNSuXTscPXpU1s4Rke2kTB1Q7dYhLhSRWg3MhbQqVF4AO8Q5d+pADFeOeIjhytFRRzx2vUC1rO3kInlkJjMzE7fffnuN29VqNQoLnbuunIjMU+o3KFIeby8VZgxOxJPLD0IFGI3m6QOcGYMTFTeCp/R8MFeOjjrqsct1OlnbyUXyyExcXBzS0tJq3L5x40YkJjp3LwYiMo9TByRFv6RILBzdBhFa4/dDhFajyGXZ7pAP5srRUUc99uoD1vdlktJOLpJHZv73v/9hwoQJKC4uhiAISE1NxcqVKzFnzhx8/vnnjugjEdlAP3WQnV9s8tuZCpUXKiVOHZBr9EuKRJ/ECMUv43eXfDBXjo466rGPZ9+QtZ1cJAczjzzyCMrLy/HCCy+gqKgII0eORMOGDfHhhx9i+PDhjugjEdnAXacOyLW8vVSKX35t71JyZ9VdcuXoqKMeWyeImz4S204uNtWZGTduHMaNG4erV69Cp9MhPDxc7n4RkQz0UwfV8woiFJRXQCSVPaMOzsyzceXoqKMeu0xkLozYdnKRnDPTq1cvXLt2DQBQr149QyBTUFCAXr16ydo5IrJfv6RI7HyxF1aO64QPh7fGynGdsPPFXgxkyG3ZOurg7Dwb/egogBorxRw9Ouqox67j5ytrO7lIDma2bduG0tLSGrcXFxfj999/l6VTRCQv/dTBkNYNkRwfxqklcmu2LCW3lmcDVObZyL2025WJ1Y547Pp1/GRtJxfR00xHjhwx/D8jIwPZ2dmGnysqKrBp0yY0bNhQ3t4RERFVY0s+mCu3bHBlYrXcj32pQNwUn9h2chEdzLRu3RoqlQoqlcrkdJK/vz8++ugjWTtHRERkitR8MFfXXXJlYrWcj33BQkBoSzu5iA5mMjMzIQgCmjRpgtTUVNSvX9/wOz8/P4SHh8Pb29shnSQiIqpOyqgD6y7Jo7S8QtZ2chEdzMTExAAAdE7OUCYiotpH7PJpsaMOrLskD2ubTEptJxfJS7PnzJmDBg0aYOzYsUa3L1myBFeuXMGLL74oW+eIiKj2ccTyadZdkofa1xslFdZHXdS+zp2pkbya6ZNPPkGzZs1q3N68eXMsWrRIlk4REVHt5Mjl0+62ZYMStYsRN3Iltp1cJI/MZGdnIzKy5h+8fv36yMpy/V4YRETknpyxTYG7bNmgVPNGtEHSzM2i2jmT5GAmOjoau3btQlxcnNHtu3btQlRUlGwdIyKi2sVZy6fdYcsGpfL384aXCrBUjsdLVdnOmSQHM//9738xadIklJWVGZZob926FS+88AKee+452TtIRES1g6uXT5N1e07nWAxkgMpAZ8/pHHS5pZ5zOgUbgpkXXngBubm5GD9+vKESsEajwYsvvoipU6fK3kEiIqoduHxa+X7/64rodooOZlQqFd566y1Mnz4dx48fh7+/P5o2bQq1Wu2I/hERUS3hzsunnbUTt6vtOCEumNlx4gqmIMHBvfmXTbtmA0CdOnXQvn17OftCRES1mLsunza1lDwiWIMRHRojtl6ARwU3lnKabGknF1HBzNChQ/HFF18gODgYQ4cOtdh29erVsnSMiEjpasu3cWeSuk2Bq/10JAvjVxyscXt2QTE++OUvw8/21smxl1zvVT+RBV3EtpOLqGBGq9VCpVIZ/k/ujydhIvs4orAbVXKX5dM/HbmIp1YeEtVWXyfHFfVs5HyvRtX1x6UbZaLaOZNKEJxddNi5CgoKoNVqkZ+fj+DgYFd3RxF4Eiayj76wW/WTp/5SywJsnm9TehaeWF5zRMYSfc7Pzhd7OS0wk/u9+tLqI/g69bzVdqM6ROP1oS3Fd9QEKddvJw8Ekas5sromkS0qdAJSTuVgXdoFpJzKQYW1dZ8uZq2wG1BZ2E3pz4Ok079X1xz8G9PWpEu+f9U6Oc7giPdqTFigrO3kImqa6fbbbzdMM1lz8KC0SJWcxxnVNYmkcMdRQmcVdiNlMfVetZWz6uQ44r0qmLyC2N5OLqKCmXvuucfw/+LiYixYsACJiYlITk4GAOzZswfHjh3D+PHjHdJJkgdPwp7DE3KezA1/uzK3QIzaXNjNE9531ph6jlsysk2+V21lrk6O3K+v2Pdgdv5NpJzKEfW453KLRB1TbDu5iApmZsyYYfj/f//7X0ycOBGvvfZajTbnz1ufRyPXqc0nYU/ijqMZ1bnzKGFtLewm1/tOyQGRuSXWxeUVsgQylurkiH19pbx+Yt+Dr/14HLmFpRYfV+9yQYmoY4ptJxfJdWa+++477N+/v8bto0ePRrt27bBkyRJZOkbyq60nYU/irqMZ1bnzKKE7F3azlT3vu6oX3zNXi7Ay9RyyC5QXiJt9jgXyfLmzVCdH7OtrOthS/1PPJrBGcGPtvapXNZAx9bhV1Q8SVyBXbDu5SA5m/P39sXPnTjRt2tTo9p07d0Kj4UVQyWrjSdiTuPNoRnXuPEroroXdbGXP+05MnokSAnFLz9FWWn8f5N8sN/xsrk6O2NdXpxMwYcUhE8FWCT745YTh56rBoaX3qiXm/q4VOkH0Z9LZb3/JwcykSZPw5JNP4sCBA+jUqROAypyZJUuW4JVXXpG9gySf2nYS9jTuPJpRnbuPEiq5sJu90zjV76/TCaLfdx3iQo1GYeb+8pfVC6gSAnFrny0p9MGEtTo5+td518krol7fl9eliwpGqgeH5t6roYG+yC00Xy+m+vlEagJ0oMbmDQZsIvnRpkyZgiZNmuDDDz/EihUrAAAJCQn44osvMGzYMNk7SPJS8kmYLHPn0YzqPGGUUImF3ezNazF1/xB/X1GPvSUjG5O/TbMpKDAViDsyt6b6se2dSgoN9MX0Qc0REWzcT3NfKmxZGWUp8KjKVHBo6r2aXVCMZ79Js3q8y9eLzU6DWZJ9TeE5MwAwbNgwlwQuc+bMwbRp0/DMM89g7ty5Tn98T6HEkzBZ5+6jGVV5yiiht5dKMaNg9uZTmbv/tZviLqJLdp2R1F9T9IG4I5PcTR07NFBcwFad/t35xr0tRPfLlsBAKlPBYfX36odVtlqwpF4dNZ7/7rDk/jr7S5VNRfOuXbuGzz//HNOmTUNubmXxn4MHD+LChQuydq6qffv24dNPP0XLlvZVFKRK+jf2kNYNkRwfpviLBv07mmHuL6VC5Qnf2aMZtha9048SRmiNg68IrcZtEpmVwp7iaBU6AbtOXsWU74/afIGV6/QRHqRxaGFPc8e2NuqhAlA3wBcRwcZJrRFaDT4e2QZafz+r7385Xmep9Euuq/etQidgZeo5q/eP1GqQcTHfptG2q04OZiSPzBw5cgS9e/eGVqvFmTNn8N///hehoaFYs2YNzp49i2XLlsneyRs3bmDUqFH47LPPMHv2bNmPT+QOlDiaYe83aI4SysPWfCq5CsHJUew4UqtB25i66P7Obw5Jcheb5GvuszVnaIsa79W8whK89qP197+cBfekMLfkWuvvh2wRS6evF5fj9Z/+sOmxnb1RkuSRmcmTJ+Phhx/GiRMnjFYv9e/fHzt27JC1c3oTJkzAwIED0bt3b6ttS0pKUFBQYPSPyFMoaTRDrm/QHCW035aMbMntzP39XOXuVpE4cDZPdFAmdURQbJJv3UA/o5+rfraqvlfzb5ZiwopDVt//rnydzS25Fvt+uVFSbr2RGb4+3jbf1xaSR2b27duHTz75pMbtDRs2RHa2uBdIilWrVuHgwYPYt2+fqPZz5szBrFmzZO8HkVIoYTTDk5aJu4Kcya0VOgFr0y6Kartk1xl0iAtFn8QI2Zci22v94Sw0i9SKavuLiWTjSK0G0wcmom6gn8nXVWwOx/SBCYjQ+lv824h9//dq1kBRr7O+b+tEvl/sUVpe4fDHqEpyMKPRaEyOdvz555+oX7++LJ3SO3/+PJ555hn8/PPPomvYTJ06FZMnTzb8XFBQgOjoaFn7ReRqrk489aRl4s4md3JramZujW/g5ugvskFqX8WMyOhl5Rcj94a4FTCLTSQbZ+UXY/wK470Bq76uYhPjI7T+Vt+zYt//X6WcccjrfG/rKPRoFm6yCGGwxgcFxeZHVAQAOYWlCA30RV5hmcMCrYt5zt3OQPI005AhQ/Dqq6+irKwyYUqlUuHcuXOYMmUK7rvvPlk7d+DAAVy+fBlt27aFj48PfHx8sH37dsybNw8+Pj6oqKgZ+anVagQHBxv9IyJ5edIycWdyRHKrlNdYf5FNOX1V8uPYQj+e0T+pgaj2OYUliAi2nOQuZQCr6usqZwK92Nd8/WHHLIq5v100hrRuiGd6N8UrgxKMVmNZCmSqurd1QwAw+3rYSxC5ObVcJAcz7777Lq5cuYLw8HDcvHkT3bt3xy233IKgoCC8/vrrsnbuzjvvxNGjR5GWlmb4165dO4waNQppaWnw9nbunBwRVfKkZeLOYs+KI0tse40dc6EJqlYoLUKrwaLRbfBQcpyo+y/YdtqwD1L1HuoTc6W8PFVfV6AyQd4SsQn0oQF+VtsAwOG/5c/ZrBvgi05NKkeONqVnYcKKQ6Jr0FTVOzHCZP6drcvUq2sQ7NzPvuRppuDgYOzcuRO//vorDh48CJ1OhzZt2ohKzpUqKCgISUlJRrcFBgYiLCysxu1E5DyeUPTO2Rw1NdchLhQRwWpRq1P0kuPD8P3Bv2WfArleXI7QQF/c27oheidGGPJNKnSCqD2CACC/qPLCrA3wxbWify/SEVoN+idFSK5nU/V17ZcUice6xeGz3zONgiIvFTCua5zowoLTVqdL6oOc5gxtYXhNbcnHqfrZ9PZS1ci/068oE/O3sqRjrHM/+5JGZsrLy+Hj44P09HT06tULzz//PF544QWHBDJEtrC15glJo18mDpj+Bg24R9E7Z3LU1NyWjGwUl+tEtdVPpXRqEmZ1lMJWeYVlWLLrDPJvlhr+/vr3i5hPo35URu2twksDEvBQcgymD0zA9v/1RJ/ECJv7pa9k++mOzBqjO4IAfLoj0+o036b0LDyx/CByi8TlKMltbJdYQ8BlyxYMpj6b+vy7QS2jAAAb07MwvH203bk0zn6NJI3M+Pj4ICYmxmSuirNs27bNZY9NyubIqqG1jZjVNtwaQxpHTM1JqSZb/ULWJzECz/a+FZ/sOIWiUvnO6VWndmxdzSYAuHS9FK//dNxw2+c7MzGoZSS8VLbVtakXqMbz/2e6kq2YFXgVOgFTVh+V/sAyqhrM2ZKPZu6zaercqVLZVyumsET61Jc9JE8zvfzyy5g6dSqWL1+O0FAOIZMy2FvKnf4lJShUwjJxW5gK1gA49HnIPTWnv7iKvd5UvZCZvHhB/K7K97SOsrocvOqUmX5KxB5Z+cX47PdMyffTv65Qwa5pvj2nc4ymvZzJ1HtDbNA7fWAC6gWpzb6nzZ077S16d71E4Uuz582bh5MnTyIqKgoxMTEIDAw0+v3BgwfN3JPIMVjzRD62BIVilok7ctNAqUxuphjgi/IKHW5UOQFXD+DsfQ5yV3Ce/+sJURfXp3regi631DP01+zFS9SjVmpU119Uuy0Z2UiOD5N1V2opqr6uV0Uu+974z1RT1Xyf1MxcfLlbeiAlB3PvjbzCEoujVPoA6OEucWbfU7bm3Yihc/IUv+RgZsiQIVA5eckVkSWseSIPRwWFSpr+M7uZoomgIKtKAAdAlucg19RchU7AUpGJsE0b1DHaidrei1dEsBrJTeph/m+nrLZdl3YRLw1MxC8iK87KrerrmnIqR9R9lqWcxbKUs4jUanB3q0isP5zl1ECs+vSOqfeGfhWTtb+jteDYkUFm/k3bqwfbQnIwM3PmTAd0g8h2rHkiD0cEhUqa/rPlQi4AmLr6KPJMBDu2Pgc5puZSM3NF72ZddTpCjovXiA6N0Sk+DKGBvlaXBOcUlmL+rydMFrlztKd6xuPZPrcZXldr03zVZeUX45Md0kdjht7eEGsOXbA5YPx4RBujKsZtY+riwNk8rEu7YPjZ2vvYSwXMH2H9fenIc6IgiEtKl4voYKaoqAj/+9//sHbtWpSVlaF3796YN28e6tWr58j+EVnFmifykDsoVNr0n60XclOBDGDfc7C3grPYv0EdtQ/axtSVfD9LcgpLkZqZiyGtorB091mr7cWOIMnN19vL6G+in+Z7YrnjUiFC/H3wzn9aoW/zBpi2Jl10ZWa9sV1iMaCl8QhM93d+M3rfhgb6WT2uTqi5x5Qpjjwn+vk6tw6c6KXZM2bMwBdffIGBAwdi+PDh2LJlC5588klH9o1I1FJrOSt71mZyB4VSRnocrUInYNdJ+aveOvM5VCX2b3CjpBzd3/nNsORYjovXspSzGPHZHqw7LK5asdgRJLl98MsJmyoq20WlwpaMbPRLisSuF3vVKCJoTdXVSuaqRYsNkMQErh3iQhESYLlIXvUYXe0tLmhvLnKfLbmIfqVXr16NxYsXY/jw4QCA0aNHo0uXLqioqGAlXnIIsbkWcidW1lZyr7ZRyvSfqfeR3KQ8h+qJxPppBClTTlKmTKpOh/VJjECkViPLa5EncdTB2aqPmsmxosqa/KIyPLn8IB7rFof1h7NwXeTWAtU/W3LkNokJXLdkZFtNIp8/4nbUDVQb3p8r9p7FD0esB4k+3pI3GLCL6GDm/Pnz6Nq1q+HnDh06wMfHBxcvXuRGjiQ7qbkWrHliP7mDQqkjPY5Y8SSlDos9xD5XU4FV9RUpYldRmftbVVd9OkyuqRall6OsnuPljBVV+tdESq6Nqc+WvX0NCfC1+qVDTHBXN8AXdyVFGn0OV6Van1qspNDVTBUVFfDzM56D8/HxQXm5czOWyfPZmmvhrjVPlETOoFDKSI+5UbjpAxOMvhVK+Xs6ctlpVSEBvmgbUxcpp3Jq9LNqIHLmahHm/vJXjf5UnzmtGrADlldRmfpbmaK/sO85lYMgjS9aRwcj7bx8+waJSQZ2Ff2o2c/HnDzlJJKpz5a9o5XXisoM013miAmY8orKaiT8i91zSbF7MwmCgIcffhhqtdpwW3FxMZ544gmjWjOrV6+Wt4dU69izqsbexMrawtIoiFxBodiRni0Z2SZHT7LyizF+xSGj26Qsh3ZWbZNrRWXo8MYvRsP19izr1QfsU1YfRX5RmdnRyUm9b0VsvQC8e38rLNtzBpuPXbJ67Ee/3Cd66wMppg9qjtwbJXjtx+PWGztZeJAGPx3JwhcpYkcUnOPRLrFG+1dVZW9uk5jEdFungU9duS7qfmLbyUV0MDNmzJgat40ePVrWzhABlfO4YnCptW3E5CLJFRRaG+npkxiBO976VfToSXZ+MZ5YfhDP9m6K2HqBFgMtZ74/qucd2LqsV08wccyqvwOAD375S/JxHRHIAEBEsKZGoqgSeKmAX/+4ZFPlYHMC1d4olKG67U/p2Zg20PS0rdRl5NWJKaNga8L/pQJxuVJi28lFdDCzdOlSR/aDCEDlhVbsrrhcal2TtbwTV9R9sTTSk3IqR9LIxb8X8hOG28yN1vD94Rz+vl64mFfksFVLzRrUwR+Xbth0X50AWQMZAFg0qi1e+P6I3btKWwo2rI1qin3cZSlnzJ4LrO22bi7hX+wKLakruezl3EcjskDKagMuta7J2oiLK+u+6Ed69MHWhiMXER6kQXaB/aMnWf+M1iwY2caoRof+260ryujXJjfLdHju/4447Pi2BjKOEOLvCy8vFaYPTMCEFYfsCjQAy6OHlkY1h7VrhA+3nrR6/I3p2diYXjnSXT3o35KRbXG1lQCgf1Lll5CqgZDGR9wqJbHt5MJghhRDSo4Dl1obEzPiovX3c+q2D9VHifIKK3MqjAuAWa5xIcVTKw9iPm7HgJZRACoDqOkDE2rk3RDZ6trNMoz6fC8itRrD8uvqgcbdrSJFTzHWC1Rb/L25Uc09p3MAWA9mqqqeWC5mRduSXWewZNcZo0Dossg9rsS2kwuDGVIMsTkOj3aJ5VJr/BssZBcU47UNx6yOuLzQr5mo41b9O9iyXLpCJ2D+ryewZGcm8q3U2ZBzBYxOAMavOIRFXirDSNSFaxyVIfll5xfj0x2Z+Hik8dYD+s+Hv68P5m49Yf1A1T5K5j5v1b9ciN00syr9uWDm+mM1H9iKqoGQ2Hs6+6smgxlSDLE5Dr2rVMl0Z/bUVZFSCE4/4pIr8gSo/zvYskHkpvQsTFl9VNRuzo4y64cM6HQCpq1Nd2k/yNjEnvGYJ2JzSjH6JTXA9j+v4GaZc/f/0dMHBq/9mIGdL/Yy+txW6ASUi9wxumpQIuXzZms+mACYzZGxdj/9l6LGIda3SQCAOn6cZqJaSu4KtEpmz07SthaCCw30k1T3RWqi8E9HsjB+heP2vRHL1JJucj2djN/Vm9QLRMrJHJcFM4DpaVmp1aarfnGQ8nnLKyytUWzR0fTPt46fuIr/pU7+0zg3dCKyQJ/BD9QcovSkbQnM7bmiP3FZ2k/GnkJwEVp/Ua8vAIuJwvrfV90n66cjF/HUStcHMqRcp2WsO7Jg22mrU5jOop+WNfe5NqfuP1V6K3QCZq4X/3nblJ6FCSsOOjWQsYXGR6EbTRI5gz6DP0JrPIwaodU4ZNmws1lbUQTUDBSqsqUQXNWNNi29vh+PrEwS/mDLX5I2iNyUnoXxKw4p/uRKrvVTuvWifu4oPEhj05eMknId9pzOwUdb/7K4qq/q581ZVa0tCasjbppJbDu5cJqJbOaIvXQAz96WwJ7qxoD0QnCmRrT6JEYgSOOLlFM5AAQkN6mH/JuleO1HaZsxXr5ejAqdgBe/d9yyXPIsKhUgeEjQq5+W1ekEq18ATCkqrcCoz/eKbn/5erFdVa2lLhs3df8IrQYqlbjz8FUnb0TKYIZsYk/OhxjuuC2BmODO3p2kpSb+NQhWY+bdzQ1/E1N/t+V7z9mUKBsepMFHW08g/6YyhvtJ+TwpkBEA3CyrwKjF4gMSe4QHaeyqah2h1WB4+2ijgpNiVf1S9Ol2cUncJaX2V0mWgsEMSeaKKrKuInb0SWxwZ2sJcb3Kqp3ii829c38r+Hh7YV3aBWReKTS5XFRqIKP/hpZXWCpu+SmRhwkJ8EVeUZnTVsvpp4n1U7u2mD4wEXclRWDVvvMWqxfXDfCF2sfLaNVT1c0w54ncRqO4jMEMKZgrq8g6m9gARUpw1zamrtXH9VKZb7clI1vSSeLplYdkLTOv/0b6n7aNMPnbNNmOS0Tm6aeJbd2zSb+M/K6kCKubv84Z2sLiNL/GT1zYILadXJgATJJIyflwZ2JXHElN6BUTAOgE4MDZPJN9emL5QUnBidz75WgDfBHo5415v5502KaFREqX58T6Rc/c2RRafz+sS7uA1MxcTB+YAEBaUbqq52Uxiyz00/xDWjdEcnyY0RfThiH+oh5TbDu5cGSGJLE358MdSBl9EhvcfbErEztPXsVvf14R1Yfq00gVOgFTVh8V/RzklhQVDC8v4MjfBS7rA1FtE+DnhW/2nceHW403VjW1lYIY+vOyrYssKnQCCkvE5cjVUXOjSVIwe3M+3IGU0actGdmijvnaj8cl9eG1Dcfg7+tlmJ7aczrHpdVs0y8yiCFytqJSHYpKjc9FWfnF+GRHJj4acTsuFxRLOrdUPS9LXWQhtSBg1rVC0ceWA6eZSBL9nK25+L1qTRN3JXZU6ZeMbCzZdcYhfcgtLMOTyw/ipyMXkXIqB1/udszjEJF7embVIfydVyS6vaVcPGukFgQEgMNOHsXlyAxJoq/SaymBzN2r9IodVVq177xD+yEAeGoli9ERUU06AVi6+6yk9gfO5pkdjTG3ctPWQn1i96eSC4MZGzmqYJw70CeQVR9yjJCxzowriV0xUOiEOgoMZIjIEil7NJkbdba0clPr72dTob4AX240qXiOLhjnDjy5Sq+l0SciIiWR8oXn6vUSVOgEo/O0tdISj3SJtalfjcMCbLqfrVSC4Ck1GU0rKCiAVqtFfn4+goOD7T6euT+8/q3hSQXjlEbO0bAKnYA9p3KQcvoqgMpEuE5Nwmp8yGeuzxBdoI6IyBUe7hyDZSlnRQU2Vb94V+gE3PHWr2ZHXlQAQgP9kGPD1gSNQjTYOeVOyferSsr1myMzEtSmgnFKI+do2Kb0LExZfdRoddD8304iJMAXbw5tYThev6RIBKl9nVaunIhcQ4XKfaPcdVr3ruaR6BAbhvErrO9cX7WYp7UpJAFATmEpQgN9kVdYJm0jzTLnbnPC1UwS1JaCcUojtoCd2GM9sfygyWXO14rK8MTyg/jwl7+wLu0CUk7luHW9HCISRwAwrmtcZVBj4zGSm4hbKVQ3wAchAb42P051+tWjA1pGYtHoNojUWl7AULWY5+Zj4s6d97ZuCEDaa6OS7RmKw2BGgtpQMM6SCp2AlFM5hgt9hRO+xkitsGvtWDPXH7Pa7oNfTuCZVWkY8dkeyfVhiEgcpY1d3964rsnKuJFajSHQsSTldM2q3dWFBvph77Q+eHNoCwA1XwN9MBXiL37SpOrq0X5Jkdj5Yi9DlWBzDMU8Ra6G8vfzwaTeTdEgWC26X/4smqdctaFgnDmuSnqWMhpmrQBUamau0eZpYuQ5eRt7otrC1lwMR9CnCGz/X08EaXyRcioHgIDkJvXQKT4Me07l4LPfM+1+nLh6AfDz8bK6IhQAnlhuecqo+rS4nreXCqF1xAcdYsz/7WRl/4I10Gp8kF9sfQpJ5eRolcGMBNaW7Op3E3bngnGmuHKXbDlHw2wZMXPTKXQiReqX1AC31A+CTtDhQl4x1h2+KOvxNT5eeLbPrahfR43XfjyGvCJxeRv6L0Wd5mxFbpUA6/uDFzBjcCLSL+TL0r+DZ6+htFxnCGgsrQhdNLpNjdw+AAj088Zj3eLxVK9bTOZmbkrPwqs/WB+BtsWlAvEbXIb4+zqkD+YwmJGgNhSMq87VSc9yjoZ54ogZkTvZezoXm9IvOez4xeU6zNn4B0ICfPFAu2h8uqNyNEXsBTi32kiR/gtb+1jbKudWJwD4KuUMHu3aBMC/WwroV2puOHLRENTogx1rqy6rMvfFUy5Sjuvv6+2gXpjGYEYiTy8YV52c0zy26BAXiohgtcXpodBAX2QXFCPlVI7F5dpijkVEjuOs3aavFZXhkx2ZeNzGDRn19Bfv1DPW82HEOptrvAWBqSn8EH9fPNIlFk/1aoouTeuhS9N6Vo9ra6VeRwkJ9HPq4zGYsYEnF4yrTuzUzK6TVx3yWmxOz8aNEsuVdnMLy/DsN2kALOfxeHupMPPu5lbnoolI+QL9vFBYqrPYZl3aRex4oRf2ZeZiwoqDuHbTdZu16sWE/ltMztxIyrWbZfjglxNYuvsM3hzaQtT1xtoXT2c7nnXdqY/HYMZGUnccdVdip2b0CWKAfInBc37KwCc7pCXdZeUX44nlB7FgZBsMaFnz8fslVS5fnPztYRQ5YTsCIpKfxsd6IAMA2QUlOHA2D12a1kP7uLrYknHZCb0zz0sFPJgcC0DcSIq+XERIgK9R7oypc6yUnEAVAG2AL/L/OaYjRnPKyp17fuXSbLLI2i7ZpthS/6W6n45clBzIVDVh5UHMWp9udgn5TQYyRG6ruNx6IKN3+XoxbpZW4JfjjglkvFTA/OGtrdZ3AYB+zSOwMT0LKadysOd0juiRlOpJwKbOsVJzAt8c2gILR7eRtNxaCubMkKLYsk+RvYnBpeU6vLj6qA29rdKHf3aUXbr7LEL8ffFw5xi0jw1Ddv5NzNygnHllInKsM1cL0eGNX+CojXt0AhAWpDGcJy09zE/p2fgpPRsAoLVjtY+pc6zYDXIjgtWYeXdzw6hOkMYXoz6Xv8p5gMa5wQxHZsgqfdJz9WJSlpiqhiym6N6m9Cx0mvMLrouoYyDWtZtlmLv1JEYt3ovn/u+IrMcmIuUK8ffBB7+ccPhn/vL1YsN5UswIDQDk25m/U/0cq//iCZgvSPhs71uxa8qdRtNTV284ZkHEmatF1hvJiCMzJEq/pEj0atYAX6WcwdncIhTcLMPaNOs1IvTzuNaK7lXoBMz/9QQ++OWEw54DEdUyTqrcpp/i0Z8nq9ercaRfMrIN+ZvmVttWP9fqt2oJD9KgnswF9vR8nLwghsEMiWIqGBEjPEhjtejeY93isC4ti7tTE5EsQgJ88UjnOHzwy18OfRxThVIPnM1zWiADAGvSLmDaQOMtDcytfjJ1Ho8IViPkn2RgOWfi1N7OnfhhMENG9MWbqn4INqdnYfyKQ5KOo/+Qt42pi+7v/GZxbyV7En2JiKp6aUAzjL2jCTYckbe6cHXmCqU6e2++3MKyGnW+TK22Nfel8lJBieE2U8VgBaDGaioxfLw5MkMuYrJ4U5Xle2JV/ZAfOJunqNoHROSZ9F+gxt7RBN5eKodX/DZXKFXOx1X7eKFExMotawGUmEru2gBfaHy8jUbI9c+x6kjP7B+O4UqhmGsCgxmPZGrEQ0lF9swWb7KhYqdKBYzrGod+SZFYl3ZBng4SEZlhapQkr7AUXqrK1UZymT4wAfWC1BbP4W1j6iI00E/UVJMKlV8Y1T5eRpXJ9RWA28WEYtRi6yuNzlwttPh7MZXcrxWV4etH28DLS2XyOqUf6dmacQnrj1gvu3F7Y3m2gBCLwYwTuGrHabHkLoOtE4BPd2Ti9sZ1uR8SETlc9VGSTelZmLBCvj2K9KM+D3eJs/glVH+uFxvIAMAcCxV+K3QCIoI1VvMJV6aew1O9mprtm9ipr6uFJRjSuqHFNne3bigqmLnbynHkpuil2XPmzEH79u0RFBSE8PBw3HPPPfjzzz9d3S1J9CMe1aNiOQrLycURZbAFANPWHEXr6BAE+jm33gAReTYVgLBAP3wwrBVWjuuEnS/2MgQytnw504+QqFBzckTsJsLmzvXmRGg1WDi6DfolRRpyXIa0bogOcaFIzczFurQLSM3MxfD20VaPlV1QYlQGozo5N+z95Xi2qGOJbScXRY/MbN++HRMmTED79u1RXl6Ol156CX379kVGRgYCAwNd3T2rXL3jtFiOSljLLSxD+9e3oJDVdokkuaV+IE5esTx1UJsJAHIKS5FbWAovLxVSM3MNoxlSv5zpz7xvDm0BAGY3Ee6TGGG0pLnqFIzYAGpiz3jENwgyO01lbtNJMSydx60V1DO1KsucvRaCJlvayUXRwcymTZuMfl66dCnCw8Nx4MABdOvWzUW9Es/ZO07bmpdTL9AxdQYAWN0kkohq6ts8AuVHs3Amx7mFx9zNaz8eN/xfP3UvJmG2qupTVKamfLZkZOOOt341myogNoCa99spLBrdxuT53tKmk2JYGlWxVMld7MiTno/I2j1i28lF0cFMdfn5+QCA0FDr0aMSiB3xkGNkRGpeTtXA53KBYypAEpFtFmw75eouuB391P2k3k1FtX+qZzy63FK/xpe+6suardXJWji6jaQAytRovD15i2JHVcwV1DO3KsuchnX9cULEqGHDuv6ijicXtwlmBEHA5MmTcccddyApKclsu5KSEpSU/HtxLigocGi/LI2GyDlPaYmYD1vVN6qtBfCIiBxF7a1CSYXtKbv6qfuVqecQEazBpQLLUyrP9rnN6kiE2FSBd+9vJbqfpkbjbc1blDqqYqmgnlhit4Zw9rYxbhPMPPXUUzhy5Ah27txpsd2cOXMwa9Ysp/TJ2miInPOU5kjNyzEX+BARuVKvhAbYmG5f0qiAymTYZ3vfirm//GVxSgWA2RwYPbGpAlBVnvvFBiTVR+PFjs6H+PsaTTtJHVUBTBfU8wRuEcw8/fTTWL9+PXbs2IFGjRpZbDt16lRMnjzZ8HNBQQGio61ng0sldjRErnlKUyp0Ar7YlSk6L6dDXKisS7CJiOQyulMMmkUEybI/W2y9AItTKgAs5sDoiV7SfKMEMwYn4onlB0W1rz4aL3Z0/uNRbeClMl0Hxllui6iDA+euiWrnTIoOZgRBwNNPP401a9Zg27ZtiIuLs3oftVoNtdpxCa2AtNEQc/OU2n/2DumTGGFTH6ROFV2+XuyQJdhE5F7qBvigVaMQbPvrqqu7YqBSAe1jQ9GpSRhW7D2HS9fty+MLD9IgOT7MbDKv2Gl5KakCyfFhWDCyDZ5aedBsoT5zo/FiR/E7NQlzebHVdjFhWJH6t6h2zqToOjMTJkzA8uXLsWLFCgQFBSE7OxvZ2dm4efOmS/slZZUSUDlPufPFXni2d1PDMrtrRWX44Je/cMdbv0quNSO1ngFQ+WGzJdG4boC4ZYFE5B6uFZXbHciozPzfVoJQuUGjt5cKs4Y0t+tYIf6+hmChav0W/dSKpS+i+t9X/BONdIgLRYiVc2BIwL+PN6BlJOaPuN1kO0uj8frVRlXbibmfKzTQigvwxLaTi6KDmYULFyI/Px89evRAZGSk4d8333zj0n7ZskppS0Y25v5yosYyO6nF86RmvatQOXzaIS5U9LeMe1pHYUKPeLw0IAGvDErEs72bQqtR9CAeEYmkP3d4qWwPRCK0Giwa3QaLRrdBRLWLVkSwGs/2booPh7fG1//tiAk94kUd8/L1YlToBGj9/fBol1gEqm0rtvlIl1izF32pX0TFqP5IA1pGYdHoNois/rpUKZJnin4Uv8braeV+Tif24uPkfAZFX6EEQZnZHVJXKclRPE+/amrXySuSRmQEVO4n4u2lsjqUqbc2reZus6EBfqIfk4ikqRvgizwb9kGzh34qpHo+nyVdm9bD+B63GOVqWFsd46VS4WMRS83PXC2skccSGuiHjnF1EV8/CB3jQvG//ztstMtzdXUDfPFUL/NLs6V+EU3NzLW6P11eUc1dq21dNSTHaiNHu1oobgpQbDu5KDqYUSqpq5TsLZ5n71Lq1348Di8vlSEhWWySWlW5Rdb3GiEi0yztfqxC5f48ADBz/TGjDQdtFRLgK2qT2LFdYrExPVv0uaXHrfVrnKOsrY4Rc74MCfA1mfibV1iKTemXsHB0Q3S9tT5m3t3c5IIK/XHmDG1h8cIv9YuoPbXCbF01pPTVRs4qOSKVoqeZlErq/KY9Hwhb8mOqU9I+UES1UUm5DhN73YLQQOP8i8gqUwj9kiLx8oAEUcezlMumAvBIZ+uLJYDKUZWdL/bCV490sDrl5KUCHkyOFXXcqsScL82NtFTPYzE3FRMpcipGH1iZe65Vp+UB5V64XalDXCgCrOy3F+jnbVfJEVtwZMZGUqop2vqBkGs366pTWUWlzi1kRESVlqWcxRv3JqFuoNrkFMJPR7Iw8Zs0i8cI8PPGZw+1Q6cmYdiSkW2yztXw9o3ROCwAoYG+yC00PTpTdfTY20uFrrfVR+/EcGzJuGz2sZtHBePA2Tybpj0snS+Ht2+MD375y+x9q49c2zMVI7WsvzNqhbmbCp2AIiv77RWWVqBCJzh1eozBjB3Efqhs/UDIuZTaUNyJiFzi2s0yTFhxCAtHt8GQ1g2NfrcpPQvjV1if/i0qrYCXSgXvf6aNq55/zlwtxMrUcxYDA8D0RbtCJyD9guVq6UcvFGDEZ3ssbpOiP5apc6K58+WGIzVz9EypOnJtz1SMlC+icu5p5Cm+3J0put24buKSv+XAYMZOYj5Utn4gHLWbNRG5TvVkf/0IrFimLuqb0rMw95cTokZxTV20pXxxMrdNCmC9Krp+IYI+oEnNzEW9OuLqgsk5lSNldEeuPY08xb4zeaLbjXPiftAMZpzElg+E2A/vUz1vQd0AX6MdZIlIeUwl+0sdgZU6Ha1C5aqglwcmIELrb/KiLeWLk7kVmGKqout0wMvr0pFb+O+CgohgNUICfJFfVObUqRwpozuOXmVkaY8/pQm0ki8jtZ1cGMw4gNRhVnNvWrHTU8/2uRUA8PlOy1sbEHmqQD9vFFqZx3ck/YhrgJ+31XwCwDh4kBJIRNowHS0AyCksRYTW3+zFW2rphepBmZjyE899e9jk36jq6i0lT+U4apWRtdEspRnaphHWmCjfYaqdMzGYkZmYYVaxHwip01PTByaKmncn8jSuDGSAf0dYg9S+GLV4r9X2VUdXpEyf2DMdbandH9mW82WsHVNMQGXtbxQS4AuNjzeyC2rPVI650awsC1N5rtYuVtwImdh2cmEwIyOxm09KYWl6avrABGj9/bAu7QLCgzTQcusBIlFC/H1RWqETNYqiN3/47QgLqlyJVK+OGhAqC4NVHWGt0AmSk/31JfMt1YVRAfh45O12TUdbanc2t0jUMcwdU478vmtFZfj60Tbw8nLtRorOYm16UID1YqqusGLvWdHtHu3axMG9+ReDGZnIUeXXHFPTU3mFpXjtR+MAx9rafyKqlH+zMj/jvtujcL2kAteKSpFqIbFxwcjbMaBllNXjOmr1S0iAL+4y80VIjuXDUi+V1Y8pV3Lu1cKSGiu9PJWYXClLxVRdJTOnUNZ2cmHRPJk4Ys+PqqpumJZ/sxQTVtQspCflWyaRO/Dzdsw3Uv1F//tDF/FzxiWknslDSIBvjU0FI//Zg0hMIKMndY8dKSXzTZFjk8Lbo+tafHxTxxzevjE2HLmIlFM5aBtT12IxOrFqU/G57HxxGyaLbecsYv/Gzh5L4siMTOSYtxZDrkJ6RErmpQLGdY1Dt1vDMepz6zkocsj/J6B4tvetiK0XYNc0h5RkfznOHVKmo031IzLEX+QzqxwlEgCjejaRWg3ubhWJT3dkStrrqaqwQL9aVXyu6oouOdo5y+3RdfHVnnOi2jkTgxmZOKvstZyF9IiU4oNhrZFbWIKzuUWICQ3Ag8mx8PPxspqDIif9dPCqfeew88VeducpiE32l+vcIXY62tRKGf1UlaVzS0iAL8Ykx+DDrSdr/C47vxif7sjEY93isP5wVo3Hy79ZZnXk+LUhSYrKDXG0UJH1dcS2cxaxga+UAFkOnGaSidQ9P8Sq0AlIOZWDdWkXkHIqBz8f4/5K5Hne3vwHGtb1x6tDkvBo1ybw86k8NYmZQrH2OynsnQ62hZznDjHT0ab2atO/ziqYfi1VAN64pwW+3f+3ycfVB5rrD2dh+/96YuW4TvhweGusHNcJ2//X0/D3NCfQzxt3JUVYfX6eJCJYXBArtp2z6N+vlthyrbMXgxmZyDFvXd2m9Czc8davGPHZHjyzKg0jPtuDpbvFZZITOUNyXCi0GvsHeC1thmopB2XR6DZYZOZ3C0a2qZEDI5Yzq2874txhbUEC8O/mjXrWcn3qBvqJygs8cDbPEFAlx4fhwNk8qzlBhaUVTg0glUCpQYE1YgJfV9QF4jSTjOQse21umTeRkqRk5tocMFRlbcWftRwUU78DgGlrj9rUH2cnospdMl/KgoSqU2GWXud1aRdEPXb1QNBZ+YTupurKN0C5xQJNUeIWDwxmZGZv2esKnYA9p3Mw5fujDGTILVj71i2WuQusnqUcFFO/SzmVI7lvzt4FuXq18O3/64kDZ/PsrrNiTwBh7nW2NbfHWfmE7kiJQYFYjt7iQSoGMw5ga9lrU9WDiVzFSwXoXBBRb/xnqsneE6PUb/rO/jZsqVq4vbVWHBFA2FrPRo46OGK50x5HekoLCqRw1BYPtmAwoxCcViKlefSOWHz2+xnZjhcW6IccEctMl6WcxbKUs3bvTyP1m74zvw07olp4VY4IIGwtCOioQoLVudseR1UpKShwV0wAVgDWjiGlUQHYcCQbz9x5i13HearnLYZVLSlT75SUX2MpKVgMa6uEACBI440HO8Vg+sAEbP9fT6dc9GxJzpXKEUnFgPSCgPbeTyx9cChm5RbJp/pqW3ves/biyIwCsHYMKY0+f6VDXBhCA8/ZXLirY1wout5aHwAkn+js3QbE0oiA3vXiCny1p3KF4Oc7M53yLd7W5FypHJWPYeu0iBz5hKbu68itZMg8pY2EMZhRgF8ysl3dBSKTNh/LRse4utiYfsmm+/+RXWAIZsSU7a/O3gu7uQu6KXJN8VjjzNU9jsrHsHVaRM58Qv2FU+svbsm40vY4cmeOnia1BYMZF9uUnoXFu864uhtEJi1Lsa+u0fm8f/eVsefibM99q17Qs/Nv4rUfj5scaXLWt3hnr+5x93wMaxfOsV1iRR1HyUu/3SlxWakjYQxmXKRCJ2DPqcol2ERyqaP2xo0S5Ww4GhMaYPi/PRdney/s+gt6yqkci1NmzvgW78zVPe5OzIVzjcj6N0pd+q206RprnDVNKhUTgF1gU3oWurz5K0Yt3otrN+Wp0UG1m77k/dv3tTJZmdMVvFTAg8mxhp/FJORWZ+s2IOYooYCbo5JzPZGYC2duYRlCA/1k30rGGdwxcVkJnyFTGMw42ab0LDyx/CCyC5Q75EnuR0DlBXBAS9OrRlxxXRzXNc5oTx5LF3FTHHFhV0oBN0ev7vEUYi+I97SOAuBewaEzVrU5glI+Q9VxmsmJKnQCpqzmtBLJ79neTQ0XQHO7J09YYb5s+qTetyKnsERUjsxTPW9B0wZ1EB6kwa9/XMLinZlGxfW8VJWBzNQBiTXuay4ht26ALwQYVxN2RN0XJU3xuHOxNGcRe0HskxiBDnGhblVJV6nTNdYo6TNUFYMZJ9pzWnp5daqdQgN9kVso/r0SWy/Q6GdTSZ8LvSwv0005lSMqmOlySz3DsZPjw/C/u5rhq5QzOJtbhJjQADyYHGtxl2RzF3EADr+wy13Azd7ETXdPznU0KRdOby+VWwWHSp2usab6nlKmcKNJD5dyKsfVXSA3MX1Qc0QEa7Dr5BXM/+2U1fZivsFaGwmw9RuXn48XHu3aRMzTMjB3EXfGhV0/OjRzfYbRdK/Ub/HulrjpjqQGn+4UHCp1ukaMfkmReKxbHD773fSorCve/8yZcSplzX2SNGGBfhjUooFTHisiWIPk+DA82+c2i0mz1ZMbrVXk1J/sh7RuiOT4MKNvT7UvMdX4tREE8Z9Pd0zcdFeeml9kLSFe6YnLn+7IrLF3myAAn+7IdMn7XyVI+QS7oYKCAmi1WuTn5yM4ONilfdl14ipGLd7r0j6Q7cZ2icVLAxPR5c1fHZrAHRroi30v9TEEDfoLJ2D6m6n+hC7XSIGnjziYq1tS/fU0p0In4I63fjWb76Afwdr5Yi8PCvxcz51qsYgl9rOtJM58/0u5fnNkxok6xYdJ2puGlEVfBGrm3YkOXfp8b+uGRicBMd9M5Rwp6JcUiZ0v9sLKcZ0M+yrtfLGX4k6qtpBjBYmUxE2Sj6VRRXfljqNOSn3/M2fGiby9VHhzaAs8YSFxipRJBSDvn2Jr5lbkhPj74s5m9fH9oYt2PVbvxIgat1nKd3FERU53yj2QQo4VJO6auEnK5G6r2pT6/mcw42T9kiKxaHQbzFx/DNkFJa7uTq3U87Z62HcmT1KlXAHAhBUHsdCr8tuSpRU5W/+8YvOqtboBvmbnyM0FGK5e4ulOw/9ynIjdOXGTlMmdvjwo9f3PYMYF9BfCPadyMGHFQVYBdhIvFaATgN/+vGrzMaqOcJg7Adkz+mZLApsrvym5W36NHCdipdbZIHIGpb7/mTPjIt5eKnh5qRjIONiDnRpjTHJjAKiReS+V2Llg/ehbRLDa6PZQEflS14rKJM81u+qbkjuu6JFjBUntW/VF9C+lvv8ZzLgQ59Qdz8fLC1/tOSfrMcX83folRWLXlDuNkminD6pZEdfW41fliiWe7lqKXa4TsTsmbhLJRYnvf04zuRDn1B1v6e4zsh+zXh01Uk7lWM0RqT4NJbZootT3hdxVbcVwdZ6OPcwlcEstmuduiZtEclLa+5/BjAtZm3skZVEBCAnwxXPfphklb4vNEXHkXLNcF2ixlLqiQSy5TsTulLhJJDclvf8ZzLiQpW/UpCz6v0+eiVVK+hwRa8Orjh5BceY3JaWuaJBCSSdiIrIPc2ZczNzcY1ign4t6RAAQ6Odt9HOEVmO24KGUHBFHzzXLUVjM2pYIgHuXYiciz8PtDBSieq2OtjF10f2d3yzmJZDjfP1oR3h5qQx/D51OELUVxcpxnUR921dqbRYpS63dsRQ7EbkPKddvTjMphKkh77tbReKTHZku6pEy6GvDmPtZbvq8lU7VRjXWpV0QdX+xOSJKnOIwt2eRuWk0Z+fpEBGZw2BGoSp0AtYftl6nQ6vxRtdbw7HhiPJqesghWOOLR7rEIbZeAMKDNMgrLMWEFaZHA+SIcQSYzlvxhBwRS2zdEkFpKxqIqHZizoxCWVv6qjd/ZFscOJvnhB45VvUcFb38m2WY+8tfUPt4ITk+DANams85WTCyjcU8Dr0AM4+ld+hczdfT03NE7Nk8zhM3ACQi98KRGYUSO12xV2TQ4wpiR0vqBvjCz1uFwtKaeyWZGhWwNBrg5QWLq8Oe7hWPj387ZbE/n/2eief6NoOfz7+xvitquTiTuy+1toVS85aISDoGMwoldrri9JXrDu6J7Z65sykC1T54/afjFtuZWu5clakCbOZyTszlceiTWC/k3bSac6MTgK9SzuDRrk1EHdsTckQ8fRqtOnfbU4qILGMwo1BiC6ylnBZXVVZvdMfGWL5X3vL+5szdegJajXxvMbGjApZGbl5Zly7qGGdziyQf250pdfM4R5Ca6ExEysecGYXST2uYG0QQALSO1iKvqFzScdvG1EWk1nnfrvOLpfXPEimjAubyOGJCA0Td31I7T8wRUermcXJz1z2liMgyBjNubGP6Jcn3idD6Y8bgRItJsn0Sw2sEPHXUlpNm7aX193FKcu2DybGwdj32UlW2q22UuHmc3OxJdCYi5XKLaaYFCxbgnXfeQVZWFpo3b465c+eia9euru6WQ+m/Qcql6jSBt5fKZO5HaKAvZg9JwoCWUTWSI7Pzb+LZbw/L1p/qxnaJw9xfTjg8udbPxwvjusZZrN8zrmucUfKvrdwxwdRTp9H0amOiM1FtoPhg5ptvvsGkSZOwYMECdOnSBZ988gn69++PjIwMNG7c2NXdcxixS7PFMBUQWLto2brjsy0itRo81aspbosIckpy7dQBldMpn/2eWaMg37iucYbf28OdE0yVWNBPLrUt0ZmotlD8dgYdO3ZEmzZtsHDhQsNtCQkJuOeeezBnzhyr93eX7QyqW5d2Ac+sSpPlWHJcRCt0Au5461dZd/g2VfbemaMZpeU6fJVyBmdzixATGoAHk2NlGZExl2DKMv+uZ+19rB/B3PliL48ZjSJyVx6znUFpaSkOHDiAKVOmGN3et29f7N6920W9cg65vhlOH5iAh7vE2X1itmeHbxUAbYAvND7eyC6wPOrizFEBPx+vGsuv7WVrJV1yDk+vF0RUWyk6mLl69SoqKirQoEEDo9sbNGiA7Oxsk/cpKSlBSUmJ4eeCggKH9tFRrC2VFatekFq2E7OlGi53t4rEp//koZi6QLw5tIVH52LoSUkw9dSpHKXz5HpBRLWVooMZPZXK+IInCEKN2/TmzJmDWbNmOaNbDmXPSEhVcs/9W8q1ub1xXasXCE+/gDPB1D14eqIzUW2j6GCmXr168Pb2rjEKc/ny5RqjNXpTp07F5MmTDT8XFBQgOjraof10FHPfIMVwZJEzS9V3a/sFggmm7sOTE52JahtFBzN+fn5o27YttmzZgnvvvddw+5YtWzBkyBCT91Gr1VCr1c7qosNVDxDOXC3EytRzyC4oMXsfV879O/oCofTlzrWpki4RkVIoOpgBgMmTJ+PBBx9Eu3btkJycjE8//RTnzp3DE0884equOU31AOGpXk2rBDdF/wQ3/47c1A30xb2tG0Lr74cKnaCoi7093GG5MxNMiYicT/FLs4HKonlvv/02srKykJSUhA8++ADdunUTdV93XZothX60YktGNtamXURuYanhd0q72NvK3ZY7u0PgRUSkZFKu324RzNijNgQzgPtd7KXQ1wYxlzek1NogSp8SIyJSMo+pM0PieHptE3dd7swEUyIi5+BGkx7A0zfP43JnIiKyhMGMB/D0iz2XOxMRkSUMZjyAp1/s9cudzU2QqVCZXMvlzkREtRODGQ/g6Rd7/XJnADWeI5c7ExERgxkPUBsu9vpqyBFa49GlCK3GrVdqERGR/bg024PUhtomXO5MRFQ7sM5MFbUpmAF4sSciIs/AOjO1GGubEBFRbcOcGSIiInJrDGaIiIjIrTGYISIiIrfGYIaIiIjcGoMZIiIicmsMZoiIiMitMZghIiIit8ZghoiIiNwagxkiIiJyax5fAVi/W0NBQYGLe0JERERi6a/bYnZd8vhg5vr16wCA6OhoF/eEiIiIpLp+/Tq0Wq3FNh6/0aROp8PFixcRFBQElcr0hosFBQWIjo7G+fPna8VmlErA19z5+Jo7H19z5+Nr7lyOfL0FQcD169cRFRUFLy/LWTEePzLj5eWFRo0aiWobHBzMN7+T8TV3Pr7mzsfX3Pn4mjuXo15vayMyekwAJiIiIrfGYIaIiIjcGoMZAGq1GjNmzIBarXZ1V2oNvubOx9fc+fiaOx9fc+dSyuvt8QnARERE5Nk4MkNERERujcEMERERuTUGM0REROTWGMwQERGRW6v1wcyCBQsQFxcHjUaDtm3b4vfff3d1lzzajh07MHjwYERFRUGlUmHt2rWu7pJHmzNnDtq3b4+goCCEh4fjnnvuwZ9//unqbnm0hQsXomXLloYiYsnJydi4caOru1WrzJkzByqVCpMmTXJ1VzzWzJkzoVKpjP5FRES4rD+1Opj55ptvMGnSJLz00ks4dOgQunbtiv79++PcuXOu7prHKiwsRKtWrTB//nxXd6VW2L59OyZMmIA9e/Zgy5YtKC8vR9++fVFYWOjqrnmsRo0a4c0338T+/fuxf/9+9OrVC0OGDMGxY8dc3bVaYd++ffj000/RsmVLV3fF4zVv3hxZWVmGf0ePHnVZX2r10uyOHTuiTZs2WLhwoeG2hIQE3HPPPZgzZ44Le1Y7qFQqrFmzBvfcc4+ru1JrXLlyBeHh4di+fTu6devm6u7UGqGhoXjnnXfw6KOPurorHu3GjRto06YNFixYgNmzZ6N169aYO3euq7vlkWbOnIm1a9ciLS3N1V0BUItHZkpLS3HgwAH07dvX6Pa+ffti9+7dLuoVkWPl5+cDqLy4kuNVVFRg1apVKCwsRHJysqu74/EmTJiAgQMHonfv3q7uSq1w4sQJREVFIS4uDsOHD8fp06dd1heP32jSnKtXr6KiogINGjQwur1BgwbIzs52Ua+IHEcQBEyePBl33HEHkpKSXN0dj3b06FEkJyejuLgYderUwZo1a5CYmOjqbnm0VatW4eDBg9i3b5+ru1IrdOzYEcuWLcOtt96KS5cuYfbs2ejcuTOOHTuGsLAwp/en1gYzeiqVyuhnQRBq3EbkCZ566ikcOXIEO3fudHVXPN5tt92GtLQ0XLt2Dd9//z3GjBmD7du3M6BxkPPnz+OZZ57Bzz//DI1G4+ru1Ar9+/c3/L9FixZITk5GfHw8vvzyS0yePNnp/am1wUy9evXg7e1dYxTm8uXLNUZriNzd008/jfXr12PHjh1o1KiRq7vj8fz8/HDLLbcAANq1a4d9+/bhww8/xCeffOLinnmmAwcO4PLly2jbtq3htoqKCuzYsQPz589HSUkJvL29XdhDzxcYGIgWLVrgxIkTLnn8Wpsz4+fnh7Zt22LLli1Gt2/ZsgWdO3d2Ua+I5CUIAp566imsXr0av/76K+Li4lzdpVpJEASUlJS4uhse684778TRo0eRlpZm+NeuXTuMGjUKaWlpDGScoKSkBMePH0dkZKRLHr/WjswAwOTJk/Hggw+iXbt2SE5Oxqeffopz587hiSeecHXXPNaNGzdw8uRJw8+ZmZlIS0tDaGgoGjdu7MKeeaYJEyZgxYoVWLduHYKCggwjkVqtFv7+/i7unWeaNm0a+vfvj+joaFy/fh2rVq3Ctm3bsGnTJld3zWMFBQXVyAMLDAxEWFgY88Mc5Pnnn8fgwYPRuHFjXL58GbNnz0ZBQQHGjBnjkv7U6mDmgQceQE5ODl599VVkZWUhKSkJP/30E2JiYlzdNY+1f/9+9OzZ0/Czfm51zJgx+OKLL1zUK8+lLzvQo0cPo9uXLl2Khx9+2PkdqgUuXbqEBx98EFlZWdBqtWjZsiU2bdqEPn36uLprRLL5+++/MWLECFy9ehX169dHp06dsGfPHpddP2t1nRkiIiJyf7U2Z4aIiIg8A4MZIiIicmsMZoiIiMitMZghIiIit8ZghoiIiNwagxkiIiJyawxmiIiIyK0xmCEit6JSqbB27VrZj9ujRw9MmjRJ9uMSkeMxmCEik3bv3g1vb2/069dP8n1jY2Mxd+5c+TslwsMPPwyVSgWVSgVfX180adIEzz//PAoLCy3eb/Xq1Xjttdec1EsikhODGSIyacmSJXj66aexc+dOnDt3ztXdkaRfv37IysrC6dOnMXv2bCxYsADPP/+8ybZlZWUAgNDQUAQFBTmzm0QkEwYzRFRDYWEhvv32Wzz55JMYNGiQyX2z1q9fj3bt2kGj0aBevXoYOnQogMrpmrNnz+LZZ581jJAAwMyZM9G6dWujY8ydOxexsbGGn/ft24c+ffqgXr160Gq16N69Ow4ePCi5/2q1GhEREYiOjsbIkSMxatQow9SUvh9LlixBkyZNoFarIQhCjWmmkpISvPDCC4iOjoZarUbTpk2xePFiw+8zMjIwYMAA1KlTBw0aNMCDDz6Iq1evGn7/f//3f2jRogX8/f0RFhaG3r17Wx0dIiLbMJghohq++eYb3HbbbbjtttswevRoLF26FFW3cfvxxx8xdOhQDBw4EIcOHcLWrVvRrl07AJXTNY0aNTJs4JqVlSX6ca9fv44xY8bg999/x549e9C0aVMMGDAA169ft+v5+Pv7G0ZgAODkyZP49ttv8f333yMtLc3kfR566CGsWrUK8+bNw/Hjx7Fo0SLUqVMHAJCVlYXu3bujdevW2L9/PzZt2oRLly5h2LBhht+PGDECY8eOxfHjx7Ft2zYMHToU3AqPyDFq9a7ZRGTa4sWLMXr0aACVUzY3btzA1q1b0bt3bwDA66+/juHDh2PWrFmG+7Rq1QpA5XSNt7c3goKCEBERIelxe/XqZfTzJ598grp162L79u0YNGiQTc8lNTUVK1aswJ133mm4rbS0FF999RXq169v8j5//fUXvv32W2zZssXwnJs0aWL4/cKFC9GmTRu88cYbhtuWLFmC6Oho/PXXX7hx4wbKy8sxdOhQwy7CLVq0sKn/RGQdR2aIyMiff/6J1NRUDB8+HADg4+ODBx54AEuWLDG0SUtLMwoO5HL58mU88cQTuPXWW6HVaqHVanHjxg3JOTsbNmxAnTp1oNFokJycjG7duuGjjz4y/D4mJsZsIANUPj9vb290797d5O8PHDiA3377DXXq1DH8a9asGQDg1KlTaNWqFe688060aNEC//nPf/DZZ58hLy9P0nMgIvE4MkNERhYvXozy8nI0bNjQcJsgCPD19UVeXh7q1q0Lf39/ycf18vKqMc1SdeoHqFyJdOXKFcydOxcxMTFQq9VITk5GaWmppMfq2bMnFi5cCF9fX0RFRcHX19fo94GBgRbvb+356XQ6DB48GG+99VaN30VGRsLb2xtbtmzB7t278fPPP+Ojjz7CSy+9hL179yIuLk7ScyEi6zgyQ0QG5eXlWLZsGd577z2kpaUZ/h0+fBgxMTH4+uuvAQAtW7bE1q1bzR7Hz88PFRUVRrfVr18f2dnZRgFN9XyV33//HRMnTsSAAQPQvHlzqNVqo6RasQIDA3HLLbcgJiamRiAjRosWLaDT6bB9+3aTv2/Tpg2OHTuG2NhY3HLLLUb/9IGSSqVCly5dMGvWLBw6dAh+fn5Ys2aN5L4QkXUMZojIYMOGDcjLy8Ojjz6KpKQko3/333+/YTXPjBkzsHLlSsyYMQPHjx/H0aNH8fbbbxuOExsbix07duDChQuGYKRHjx64cuUK3n77bZw6dQoff/wxNm7caPT4t9xyC7766iscP34ce/fuxahRo2waBbJXbGwsxowZg7Fjx2Lt2rXIzMzEtm3b8O233wIAJkyYgNzcXIwYMQKpqak4ffo0fv75Z4wdOxYVFRXYu3cv3njjDezfvx/nzp3D6tWrceXKFSQkJDj9uRDVBgxmiMhg8eLF6N27N7RabY3f3XfffUhLS8PBgwfRo0cPfPfdd1i/fj1at26NXr16Ye/evYa2r776Ks6cOYP4+HhDbkpCQgIWLFiAjz/+GK1atUJqamqN2i9LlixBXl4ebr/9djz44IOYOHEiwsPDHfukzVi4cCHuv/9+jB8/Hs2aNcO4ceMMS6ujoqKwa9cuVFRU4K677kJSUhKeeeYZaLVaeHl5ITg4GDt27MCAAQNw66234uWXX8Z7772H/v37u+S5EHk6lcC1gkREROTGODJDREREbo3BDBEREbk1BjNERETk1hjMEBERkVtjMENERERujcEMERERuTUGM0REROTWGMwQERGRW2MwQ0RERG6NwQwRERG5NQYzRERE5NYYzBAREZFb+38er5C96mwC3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the dataset\n",
    "housing = fetch_california_housing()\n",
    "\n",
    "# Split the dataset into features (X) and target (y)\n",
    "X = housing.data\n",
    "y = housing.target.reshape(-1, 1)  # Reshape to make it a column vector\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Standardize features\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# Define the model\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Input(shape=(X_train.shape[1],)),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)\n",
    "\n",
    "# Evaluate the model\n",
    "loss = model.evaluate(X_test, y_test)\n",
    "print(\"Test Loss:\", loss)\n",
    "\n",
    "# Plot training and validation loss\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')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Make predictions on test data\n",
    "predictions = model.predict(X_test)\n",
    "\n",
    "# Plot actual vs. predicted values\n",
    "plt.scatter(y_test, predictions)\n",
    "plt.xlabel('Actual Prices')\n",
    "plt.ylabel('Predicted Prices')\n",
    "plt.title('Actual vs. Predicted Prices')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c2a528d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "9/9 [==============================] - 1s 30ms/step - loss: 31412.2656 - val_loss: 22474.9785\n",
      "Epoch 2/10\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 31404.5820 - val_loss: 22472.8926\n",
      "Epoch 3/10\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 31396.9688 - val_loss: 22470.8340\n",
      "Epoch 4/10\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 31389.6055 - val_loss: 22468.7402\n",
      "Epoch 5/10\n",
      "9/9 [==============================] - 0s 10ms/step - loss: 31381.8398 - val_loss: 22466.7285\n",
      "Epoch 6/10\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 31375.0820 - val_loss: 22464.6602\n",
      "Epoch 7/10\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 31367.5742 - val_loss: 22462.6660\n",
      "Epoch 8/10\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 31360.2832 - val_loss: 22460.5703\n",
      "Epoch 9/10\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 31352.8945 - val_loss: 22458.5137\n",
      "Epoch 10/10\n",
      "9/9 [==============================] - 0s 7ms/step - loss: 31345.7383 - val_loss: 22456.4199\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 26404.7578\n",
      "Test Loss: 26404.7578125\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 6ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from sklearn.datasets import load_diabetes\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the dataset\n",
    "diabetes = load_diabetes()\n",
    "\n",
    "# Split the dataset into features (X) and target (y)\n",
    "X = diabetes.data\n",
    "y = diabetes.target.reshape(-1, 1)  # Reshape to make it a column vector\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Standardize features\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# Define the model\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Input(shape=(X_train.shape[1],)),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)\n",
    "\n",
    "# Evaluate the model\n",
    "loss = model.evaluate(X_test, y_test)\n",
    "print(\"Test Loss:\", loss)\n",
    "\n",
    "# Plot training and validation loss\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')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Make predictions on test data\n",
    "predictions = model.predict(X_test)\n",
    "\n",
    "# Plot actual vs. predicted values\n",
    "plt.scatter(y_test, predictions)\n",
    "plt.xlabel('Actual Target')\n",
    "plt.ylabel('Predicted Target')\n",
    "plt.title('Actual vs. Predicted Targets')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d8fccd22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "11/11 [==============================] - 1s 24ms/step - loss: 627.4333 - val_loss: 566.2458\n",
      "Epoch 2/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 625.9467 - val_loss: 565.2560\n",
      "Epoch 3/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 624.5804 - val_loss: 564.2812\n",
      "Epoch 4/100\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 623.1731 - val_loss: 563.3513\n",
      "Epoch 5/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 621.8760 - val_loss: 562.3839\n",
      "Epoch 6/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 620.3939 - val_loss: 561.4194\n",
      "Epoch 7/100\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 618.9976 - val_loss: 560.4578\n",
      "Epoch 8/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 617.6797 - val_loss: 559.5569\n",
      "Epoch 9/100\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 616.2792 - val_loss: 558.5753\n",
      "Epoch 10/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 614.8513 - val_loss: 557.6597\n",
      "Epoch 11/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 613.5850 - val_loss: 556.7491\n",
      "Epoch 12/100\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 612.2698 - val_loss: 555.8661\n",
      "Epoch 13/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 610.9655 - val_loss: 554.9948\n",
      "Epoch 14/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 609.6797 - val_loss: 554.1620\n",
      "Epoch 15/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 608.5281 - val_loss: 553.3142\n",
      "Epoch 16/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 607.3207 - val_loss: 552.4996\n",
      "Epoch 17/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 606.2338 - val_loss: 551.6931\n",
      "Epoch 18/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 605.0239 - val_loss: 550.8813\n",
      "Epoch 19/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 603.7926 - val_loss: 550.0452\n",
      "Epoch 20/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 602.6622 - val_loss: 549.2819\n",
      "Epoch 21/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 601.4875 - val_loss: 548.4896\n",
      "Epoch 22/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 600.3651 - val_loss: 547.6857\n",
      "Epoch 23/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 599.2814 - val_loss: 546.9497\n",
      "Epoch 24/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 598.2422 - val_loss: 546.2526\n",
      "Epoch 25/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 597.2733 - val_loss: 545.4992\n",
      "Epoch 26/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 596.1624 - val_loss: 544.7206\n",
      "Epoch 27/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 595.0281 - val_loss: 543.9722\n",
      "Epoch 28/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 593.9539 - val_loss: 543.1958\n",
      "Epoch 29/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 592.9518 - val_loss: 542.4989\n",
      "Epoch 30/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 592.0385 - val_loss: 541.8334\n",
      "Epoch 31/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 591.1130 - val_loss: 541.1152\n",
      "Epoch 32/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 590.0646 - val_loss: 540.3384\n",
      "Epoch 33/100\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 588.9250 - val_loss: 539.5749\n",
      "Epoch 34/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 587.9060 - val_loss: 538.8198\n",
      "Epoch 35/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 586.7175 - val_loss: 538.1450\n",
      "Epoch 36/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 585.7825 - val_loss: 537.4892\n",
      "Epoch 37/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 584.8991 - val_loss: 536.8145\n",
      "Epoch 38/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 583.9733 - val_loss: 536.1587\n",
      "Epoch 39/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 583.0780 - val_loss: 535.5394\n",
      "Epoch 40/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 582.1858 - val_loss: 534.8841\n",
      "Epoch 41/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 581.3345 - val_loss: 534.2031\n",
      "Epoch 42/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 580.3201 - val_loss: 533.4730\n",
      "Epoch 43/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 579.3131 - val_loss: 532.8049\n",
      "Epoch 44/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 578.3635 - val_loss: 532.1308\n",
      "Epoch 45/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 577.4383 - val_loss: 531.4752\n",
      "Epoch 46/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 576.5651 - val_loss: 530.8154\n",
      "Epoch 47/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 575.6931 - val_loss: 530.2080\n",
      "Epoch 48/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 574.8564 - val_loss: 529.5946\n",
      "Epoch 49/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 573.9871 - val_loss: 528.9858\n",
      "Epoch 50/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 573.1009 - val_loss: 528.3497\n",
      "Epoch 51/100\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 572.2700 - val_loss: 527.7271\n",
      "Epoch 52/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 571.3813 - val_loss: 527.1448\n",
      "Epoch 53/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 570.5255 - val_loss: 526.5137\n",
      "Epoch 54/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 569.6572 - val_loss: 525.8688\n",
      "Epoch 55/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 568.7221 - val_loss: 525.2564\n",
      "Epoch 56/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 567.8715 - val_loss: 524.6282\n",
      "Epoch 57/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 566.9818 - val_loss: 524.0103\n",
      "Epoch 58/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 566.0505 - val_loss: 523.3745\n",
      "Epoch 59/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 565.0863 - val_loss: 522.7329\n",
      "Epoch 60/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 564.2350 - val_loss: 522.1236\n",
      "Epoch 61/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 563.4469 - val_loss: 521.5210\n",
      "Epoch 62/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 562.6465 - val_loss: 520.9346\n",
      "Epoch 63/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 561.8631 - val_loss: 520.3531\n",
      "Epoch 64/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 561.0628 - val_loss: 519.7566\n",
      "Epoch 65/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 560.2413 - val_loss: 519.2061\n",
      "Epoch 66/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 559.4726 - val_loss: 518.6449\n",
      "Epoch 67/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 558.6229 - val_loss: 518.0578\n",
      "Epoch 68/100\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 557.8294 - val_loss: 517.4701\n",
      "Epoch 69/100\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 557.0542 - val_loss: 516.9039\n",
      "Epoch 70/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 556.2742 - val_loss: 516.3386\n",
      "Epoch 71/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 555.5694 - val_loss: 515.7429\n",
      "Epoch 72/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 554.8022 - val_loss: 515.1784\n",
      "Epoch 73/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 554.0441 - val_loss: 514.6010\n",
      "Epoch 74/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 553.3054 - val_loss: 514.0236\n",
      "Epoch 75/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 552.5234 - val_loss: 513.4814\n",
      "Epoch 76/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 551.7830 - val_loss: 512.9506\n",
      "Epoch 77/100\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 551.0522 - val_loss: 512.4077\n",
      "Epoch 78/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 550.2940 - val_loss: 511.8415\n",
      "Epoch 79/100\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 549.4662 - val_loss: 511.2827\n",
      "Epoch 80/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 548.7664 - val_loss: 510.7404\n",
      "Epoch 81/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 548.0541 - val_loss: 510.1987\n",
      "Epoch 82/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 547.3625 - val_loss: 509.6738\n",
      "Epoch 83/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 546.7296 - val_loss: 509.1326\n",
      "Epoch 84/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 546.0088 - val_loss: 508.6029\n",
      "Epoch 85/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 545.2835 - val_loss: 508.0726\n",
      "Epoch 86/100\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 544.5995 - val_loss: 507.5617\n",
      "Epoch 87/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 543.9520 - val_loss: 506.9976\n",
      "Epoch 88/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 543.2376 - val_loss: 506.4700\n",
      "Epoch 89/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 542.5781 - val_loss: 505.9412\n",
      "Epoch 90/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 541.8466 - val_loss: 505.4439\n",
      "Epoch 91/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 541.1755 - val_loss: 504.9180\n",
      "Epoch 92/100\n",
      "11/11 [==============================] - 0s 4ms/step - loss: 540.5278 - val_loss: 504.3910\n",
      "Epoch 93/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 539.7797 - val_loss: 503.8850\n",
      "Epoch 94/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 539.1047 - val_loss: 503.3693\n",
      "Epoch 95/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 538.4163 - val_loss: 502.8358\n",
      "Epoch 96/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 537.7621 - val_loss: 502.3382\n",
      "Epoch 97/100\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 537.1185 - val_loss: 501.8278\n",
      "Epoch 98/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 536.4857 - val_loss: 501.3139\n",
      "Epoch 99/100\n",
      "11/11 [==============================] - 0s 6ms/step - loss: 535.7432 - val_loss: 500.8156\n",
      "Epoch 100/100\n",
      "11/11 [==============================] - 0s 5ms/step - loss: 535.1123 - val_loss: 500.3058\n",
      "4/4 [==============================] - 0s 4ms/step - loss: 475.7174\n",
      "Test Loss: 475.7173767089844\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 2ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the dataset\n",
    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data\"\n",
    "column_names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']\n",
    "data = pd.read_csv(url, delim_whitespace=True, names=column_names)\n",
    "\n",
    "# Split the dataset into features (X) and target (y)\n",
    "X = data.drop('MEDV', axis=1).values\n",
    "y = data['MEDV'].values.reshape(-1, 1)\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Standardize features\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# Define the model\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Input(shape=(X_train.shape[1],)),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(X_train, y_train, epochs=100, batch_size=32, validation_split=0.2)\n",
    "\n",
    "# Evaluate the model\n",
    "loss = model.evaluate(X_test, y_test)\n",
    "print(\"Test Loss:\", loss)\n",
    "\n",
    "# Plot training and validation loss\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')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Make predictions on test data\n",
    "predictions = model.predict(X_test)\n",
    "\n",
    "# Plot actual vs. predicted values with different colors\n",
    "plt.scatter(y_test, predictions, color='blue', label='Actual vs. Predicted')\n",
    "plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], color='red', linestyle='--', label='Perfect Prediction')\n",
    "plt.xlabel('Actual Prices')\n",
    "plt.ylabel('Predicted Prices')\n",
    "plt.title('Actual vs. Predicted Prices')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6ae18490",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "413/413 [==============================] - 2s 3ms/step - loss: 4.8779 - val_loss: 3.8969\n",
      "Epoch 2/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 3.0995 - val_loss: 2.4929\n",
      "Epoch 3/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 2.0072 - val_loss: 1.6408\n",
      "Epoch 4/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 1.3389 - val_loss: 1.1281\n",
      "Epoch 5/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.9397 - val_loss: 0.8303\n",
      "Epoch 6/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.7151 - val_loss: 0.6699\n",
      "Epoch 7/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5996 - val_loss: 0.5936\n",
      "Epoch 8/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5483 - val_loss: 0.5627\n",
      "Epoch 9/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5281 - val_loss: 0.5518\n",
      "Epoch 10/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5211 - val_loss: 0.5484\n",
      "Epoch 11/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5184 - val_loss: 0.5471\n",
      "Epoch 12/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5167 - val_loss: 0.5467\n",
      "Epoch 13/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5159 - val_loss: 0.5459\n",
      "Epoch 14/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5151 - val_loss: 0.5450\n",
      "Epoch 15/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5144 - val_loss: 0.5439\n",
      "Epoch 16/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5138 - val_loss: 0.5443\n",
      "Epoch 17/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5141 - val_loss: 0.5444\n",
      "Epoch 18/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5135 - val_loss: 0.5442\n",
      "Epoch 19/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5133 - val_loss: 0.5444\n",
      "Epoch 20/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5133 - val_loss: 0.5447\n",
      "Epoch 21/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5130 - val_loss: 0.5447\n",
      "Epoch 22/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5130 - val_loss: 0.5459\n",
      "Epoch 23/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5128 - val_loss: 0.5456\n",
      "Epoch 24/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5129 - val_loss: 0.5448\n",
      "Epoch 25/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5123 - val_loss: 0.5444\n",
      "Epoch 26/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5447\n",
      "Epoch 27/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5127 - val_loss: 0.5459\n",
      "Epoch 28/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5127 - val_loss: 0.5448\n",
      "Epoch 29/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5461\n",
      "Epoch 30/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5127 - val_loss: 0.5458\n",
      "Epoch 31/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5127 - val_loss: 0.5461\n",
      "Epoch 32/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5440\n",
      "Epoch 33/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5123 - val_loss: 0.5447\n",
      "Epoch 34/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5460\n",
      "Epoch 35/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5132 - val_loss: 0.5444\n",
      "Epoch 36/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5127 - val_loss: 0.5452\n",
      "Epoch 37/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5462\n",
      "Epoch 38/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5467\n",
      "Epoch 39/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5448\n",
      "Epoch 40/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5448\n",
      "Epoch 41/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5123 - val_loss: 0.5463\n",
      "Epoch 42/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5128 - val_loss: 0.5464\n",
      "Epoch 43/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5122 - val_loss: 0.5457\n",
      "Epoch 44/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5127 - val_loss: 0.5458\n",
      "Epoch 45/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5122 - val_loss: 0.5475\n",
      "Epoch 46/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5131 - val_loss: 0.5463\n",
      "Epoch 47/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5122 - val_loss: 0.5465\n",
      "Epoch 48/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5128 - val_loss: 0.5449\n",
      "Epoch 49/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5461\n",
      "Epoch 50/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5122 - val_loss: 0.5444\n",
      "Epoch 51/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5443\n",
      "Epoch 52/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5126 - val_loss: 0.5449\n",
      "Epoch 53/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5127 - val_loss: 0.5449\n",
      "Epoch 54/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5451\n",
      "Epoch 55/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5122 - val_loss: 0.5458\n",
      "Epoch 56/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5123 - val_loss: 0.5449\n",
      "Epoch 57/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5126 - val_loss: 0.5453\n",
      "Epoch 58/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5121 - val_loss: 0.5451\n",
      "Epoch 59/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5466\n",
      "Epoch 60/100\n",
      "413/413 [==============================] - 1s 3ms/step - loss: 0.5125 - val_loss: 0.5456\n",
      "Epoch 61/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5444\n",
      "Epoch 62/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5456\n",
      "Epoch 63/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5459\n",
      "Epoch 64/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5122 - val_loss: 0.5463\n",
      "Epoch 65/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5126 - val_loss: 0.5451\n",
      "Epoch 66/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5460\n",
      "Epoch 67/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5126 - val_loss: 0.5457\n",
      "Epoch 68/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5456\n",
      "Epoch 69/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5466\n",
      "Epoch 70/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5466\n",
      "Epoch 71/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5471\n",
      "Epoch 72/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5123 - val_loss: 0.5456\n",
      "Epoch 73/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5449\n",
      "Epoch 74/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5453\n",
      "Epoch 75/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5128 - val_loss: 0.5464\n",
      "Epoch 76/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5123 - val_loss: 0.5467\n",
      "Epoch 77/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5128 - val_loss: 0.5450\n",
      "Epoch 78/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5458\n",
      "Epoch 79/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5122 - val_loss: 0.5470\n",
      "Epoch 80/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5127 - val_loss: 0.5467\n",
      "Epoch 81/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5121 - val_loss: 0.5454\n",
      "Epoch 82/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5130 - val_loss: 0.5450\n",
      "Epoch 83/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5463\n",
      "Epoch 84/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5123 - val_loss: 0.5447\n",
      "Epoch 85/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5449\n",
      "Epoch 86/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5127 - val_loss: 0.5457\n",
      "Epoch 87/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5122 - val_loss: 0.5456\n",
      "Epoch 88/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5465\n",
      "Epoch 89/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5467\n",
      "Epoch 90/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5130 - val_loss: 0.5466\n",
      "Epoch 91/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5121 - val_loss: 0.5450\n",
      "Epoch 92/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5468\n",
      "Epoch 93/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5127 - val_loss: 0.5454\n",
      "Epoch 94/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5122 - val_loss: 0.5467\n",
      "Epoch 95/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5120 - val_loss: 0.5476\n",
      "Epoch 96/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5129 - val_loss: 0.5447\n",
      "Epoch 97/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5126 - val_loss: 0.5452\n",
      "Epoch 98/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5128 - val_loss: 0.5458\n",
      "Epoch 99/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5127 - val_loss: 0.5452\n",
      "Epoch 100/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5455\n",
      "129/129 [==============================] - 0s 1ms/step - loss: 0.5598\n",
      "Test Loss: 0.5598435997962952\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "129/129 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the dataset\n",
    "housing = fetch_california_housing()\n",
    "\n",
    "# Split the dataset into features (X) and target (y)\n",
    "X = housing.data\n",
    "y = housing.target.reshape(-1, 1)  # Reshape to make it a column vector\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Standardize features\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# Define the model\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Input(shape=(X_train.shape[1],)),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(X_train, y_train, epochs=100, batch_size=32, validation_split=0.2)\n",
    "\n",
    "# Evaluate the model\n",
    "loss = model.evaluate(X_test, y_test)\n",
    "print(\"Test Loss:\", loss)\n",
    "\n",
    "# Plot training and validation loss\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')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Make predictions on test data\n",
    "predictions = model.predict(X_test)\n",
    "\n",
    "# Plot actual vs. predicted values\n",
    "plt.scatter(y_test, predictions)\n",
    "plt.xlabel('Actual Prices')\n",
    "plt.ylabel('Predicted Prices')\n",
    "plt.title('Actual vs. Predicted Prices')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c880ad9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "672d5990",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "413/413 [==============================] - 2s 2ms/step - loss: 4.4881 - val_loss: 3.6272\n",
      "Epoch 2/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 2.8943 - val_loss: 2.3956\n",
      "Epoch 3/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 1.9306 - val_loss: 1.6202\n",
      "Epoch 4/100\n",
      "413/413 [==============================] - 1s 3ms/step - loss: 1.3149 - val_loss: 1.1306\n",
      "Epoch 5/100\n",
      "413/413 [==============================] - 1s 3ms/step - loss: 0.9309 - val_loss: 0.8378\n",
      "Epoch 6/100\n",
      "413/413 [==============================] - 1s 3ms/step - loss: 0.7105 - val_loss: 0.6779\n",
      "Epoch 7/100\n",
      "413/413 [==============================] - 1s 3ms/step - loss: 0.5978 - val_loss: 0.6035\n",
      "Epoch 8/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5476 - val_loss: 0.5720\n",
      "Epoch 9/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5285 - val_loss: 0.5600\n",
      "Epoch 10/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5223 - val_loss: 0.5571\n",
      "Epoch 11/100\n",
      "413/413 [==============================] - 1s 3ms/step - loss: 0.5195 - val_loss: 0.5550\n",
      "Epoch 12/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5187 - val_loss: 0.5540\n",
      "Epoch 13/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5175 - val_loss: 0.5525\n",
      "Epoch 14/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5168 - val_loss: 0.5539\n",
      "Epoch 15/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5162 - val_loss: 0.5518\n",
      "Epoch 16/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5153 - val_loss: 0.5516\n",
      "Epoch 17/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5154 - val_loss: 0.5501\n",
      "Epoch 18/100\n",
      "413/413 [==============================] - 1s 3ms/step - loss: 0.5149 - val_loss: 0.5505\n",
      "Epoch 19/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5147 - val_loss: 0.5506\n",
      "Epoch 20/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5142 - val_loss: 0.5520\n",
      "Epoch 21/100\n",
      "413/413 [==============================] - 1s 3ms/step - loss: 0.5147 - val_loss: 0.5508\n",
      "Epoch 22/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5136 - val_loss: 0.5488\n",
      "Epoch 23/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5139 - val_loss: 0.5483\n",
      "Epoch 24/100\n",
      "413/413 [==============================] - 1s 3ms/step - loss: 0.5137 - val_loss: 0.5499\n",
      "Epoch 25/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5136 - val_loss: 0.5477\n",
      "Epoch 26/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5134 - val_loss: 0.5479\n",
      "Epoch 27/100\n",
      "413/413 [==============================] - 1s 3ms/step - loss: 0.5134 - val_loss: 0.5483\n",
      "Epoch 28/100\n",
      "413/413 [==============================] - 1s 3ms/step - loss: 0.5134 - val_loss: 0.5476\n",
      "Epoch 29/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5133 - val_loss: 0.5478\n",
      "Epoch 30/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5129 - val_loss: 0.5469\n",
      "Epoch 31/100\n",
      "413/413 [==============================] - 1s 3ms/step - loss: 0.5126 - val_loss: 0.5484\n",
      "Epoch 32/100\n",
      "413/413 [==============================] - 1s 3ms/step - loss: 0.5128 - val_loss: 0.5480\n",
      "Epoch 33/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5129 - val_loss: 0.5477\n",
      "Epoch 34/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5129 - val_loss: 0.5464\n",
      "Epoch 35/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5127 - val_loss: 0.5466\n",
      "Epoch 36/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5131 - val_loss: 0.5466\n",
      "Epoch 37/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5121 - val_loss: 0.5480\n",
      "Epoch 38/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5127 - val_loss: 0.5484\n",
      "Epoch 39/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5461\n",
      "Epoch 40/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5127 - val_loss: 0.5454\n",
      "Epoch 41/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5126 - val_loss: 0.5464\n",
      "Epoch 42/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5123 - val_loss: 0.5470\n",
      "Epoch 43/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5123 - val_loss: 0.5466\n",
      "Epoch 44/100\n",
      "413/413 [==============================] - 1s 3ms/step - loss: 0.5128 - val_loss: 0.5458\n",
      "Epoch 45/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5123 - val_loss: 0.5470\n",
      "Epoch 46/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5126 - val_loss: 0.5473\n",
      "Epoch 47/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5127 - val_loss: 0.5468\n",
      "Epoch 48/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5126 - val_loss: 0.5459\n",
      "Epoch 49/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5126 - val_loss: 0.5454\n",
      "Epoch 50/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5465\n",
      "Epoch 51/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5127 - val_loss: 0.5459\n",
      "Epoch 52/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5126 - val_loss: 0.5456\n",
      "Epoch 53/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5123 - val_loss: 0.5471\n",
      "Epoch 54/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5126 - val_loss: 0.5455\n",
      "Epoch 55/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5122 - val_loss: 0.5451\n",
      "Epoch 56/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5447\n",
      "Epoch 57/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5459\n",
      "Epoch 58/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5122 - val_loss: 0.5460\n",
      "Epoch 59/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5129 - val_loss: 0.5461\n",
      "Epoch 60/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5460\n",
      "Epoch 61/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5122 - val_loss: 0.5461\n",
      "Epoch 62/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5126 - val_loss: 0.5469\n",
      "Epoch 63/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5482\n",
      "Epoch 64/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5126 - val_loss: 0.5459\n",
      "Epoch 65/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5127 - val_loss: 0.5464\n",
      "Epoch 66/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5126 - val_loss: 0.5459\n",
      "Epoch 67/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5127 - val_loss: 0.5465\n",
      "Epoch 68/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5126 - val_loss: 0.5472\n",
      "Epoch 69/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5126 - val_loss: 0.5465\n",
      "Epoch 70/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5122 - val_loss: 0.5456\n",
      "Epoch 71/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5467\n",
      "Epoch 72/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5128 - val_loss: 0.5456\n",
      "Epoch 73/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5455\n",
      "Epoch 74/100\n",
      "413/413 [==============================] - 1s 3ms/step - loss: 0.5127 - val_loss: 0.5451\n",
      "Epoch 75/100\n",
      "413/413 [==============================] - 1s 3ms/step - loss: 0.5123 - val_loss: 0.5467\n",
      "Epoch 76/100\n",
      "413/413 [==============================] - 1s 3ms/step - loss: 0.5122 - val_loss: 0.5470\n",
      "Epoch 77/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5127 - val_loss: 0.5462\n",
      "Epoch 78/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5126 - val_loss: 0.5448\n",
      "Epoch 79/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5462\n",
      "Epoch 80/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5122 - val_loss: 0.5447\n",
      "Epoch 81/100\n",
      "413/413 [==============================] - 1s 1ms/step - loss: 0.5127 - val_loss: 0.5449\n",
      "Epoch 82/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5457\n",
      "Epoch 83/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5122 - val_loss: 0.5466\n",
      "Epoch 84/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5460\n",
      "Epoch 85/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5458\n",
      "Epoch 86/100\n",
      "413/413 [==============================] - 1s 3ms/step - loss: 0.5123 - val_loss: 0.5468\n",
      "Epoch 87/100\n",
      "413/413 [==============================] - 1s 3ms/step - loss: 0.5122 - val_loss: 0.5459\n",
      "Epoch 88/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5122 - val_loss: 0.5453\n",
      "Epoch 89/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5448\n",
      "Epoch 90/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5128 - val_loss: 0.5452\n",
      "Epoch 91/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5123 - val_loss: 0.5443\n",
      "Epoch 92/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5127 - val_loss: 0.5457\n",
      "Epoch 93/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5457\n",
      "Epoch 94/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5122 - val_loss: 0.5445\n",
      "Epoch 95/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5459\n",
      "Epoch 96/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5122 - val_loss: 0.5454\n",
      "Epoch 97/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5126 - val_loss: 0.5454\n",
      "Epoch 98/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5125 - val_loss: 0.5448\n",
      "Epoch 99/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5123 - val_loss: 0.5446\n",
      "Epoch 100/100\n",
      "413/413 [==============================] - 1s 2ms/step - loss: 0.5124 - val_loss: 0.5454\n",
      "Epoch 1/10\n",
      "9/9 [==============================] - 1s 37ms/step - loss: 31544.3047 - val_loss: 22441.9922\n",
      "Epoch 2/10\n",
      "9/9 [==============================] - 0s 12ms/step - loss: 31536.0391 - val_loss: 22439.7480\n",
      "Epoch 3/10\n",
      "9/9 [==============================] - 0s 9ms/step - loss: 31529.0254 - val_loss: 22437.5977\n",
      "Epoch 4/10\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 31521.4141 - val_loss: 22435.4648\n",
      "Epoch 5/10\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 31513.7383 - val_loss: 22433.2715\n",
      "Epoch 6/10\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 31506.7090 - val_loss: 22431.1699\n",
      "Epoch 7/10\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 31499.0078 - val_loss: 22428.9297\n",
      "Epoch 8/10\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 31491.6641 - val_loss: 22426.7539\n",
      "Epoch 9/10\n",
      "9/9 [==============================] - 0s 7ms/step - loss: 31484.0918 - val_loss: 22424.6602\n",
      "Epoch 10/10\n",
      "9/9 [==============================] - 0s 8ms/step - loss: 31476.3184 - val_loss: 22422.5430\n",
      "129/129 [==============================] - 0s 2ms/step - loss: 0.5591\n",
      "California Housing Test Loss: 0.5591479539871216\n",
      "3/3 [==============================] - 0s 3ms/step - loss: 26471.5371\n",
      "Diabetes Test Loss: 26471.537109375\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from sklearn.datasets import fetch_california_housing, load_diabetes\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the California Housing dataset\n",
    "housing = fetch_california_housing()\n",
    "\n",
    "# Split the California Housing dataset into features (X1) and target (y1)\n",
    "X1 = housing.data\n",
    "y1 = housing.target.reshape(-1, 1)  # Reshape to make it a column vector\n",
    "\n",
    "# Load the Diabetes dataset\n",
    "diabetes = load_diabetes()\n",
    "\n",
    "# Split the Diabetes dataset into features (X2) and target (y2)\n",
    "X2 = diabetes.data\n",
    "y2 = diabetes.target.reshape(-1, 1)  # Reshape to make it a column vector\n",
    "\n",
    "# Split the California Housing dataset into training and testing sets\n",
    "X1_train, X1_test, y1_train, y1_test = train_test_split(X1, y1, test_size=0.2, random_state=42)\n",
    "\n",
    "# Split the Diabetes dataset into training and testing sets\n",
    "X2_train, X2_test, y2_train, y2_test = train_test_split(X2, y2, test_size=0.2, random_state=42)\n",
    "\n",
    "# Standardize features for both datasets\n",
    "scaler1 = StandardScaler()\n",
    "X1_train = scaler1.fit_transform(X1_train)\n",
    "X1_test = scaler1.transform(X1_test)\n",
    "\n",
    "scaler2 = StandardScaler()\n",
    "X2_train = scaler2.fit_transform(X2_train)\n",
    "X2_test = scaler2.transform(X2_test)\n",
    "\n",
    "# Define model for the California Housing dataset\n",
    "model1 = tf.keras.Sequential([\n",
    "    tf.keras.layers.Input(shape=(X1_train.shape[1],)),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model1.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "# Train the model on California Housing dataset\n",
    "history1 = model1.fit(X1_train, y1_train, epochs=100, batch_size=32, validation_split=0.2)\n",
    "\n",
    "# Define model for the Diabetes dataset\n",
    "model2 = tf.keras.Sequential([\n",
    "    tf.keras.layers.Input(shape=(X2_train.shape[1],)),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model2.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "# Train the model on Diabetes dataset\n",
    "history2 = model2.fit(X2_train, y2_train, epochs=10, batch_size=32, validation_split=0.2)\n",
    "\n",
    "# Evaluate both models\n",
    "loss1 = model1.evaluate(X1_test, y1_test)\n",
    "print(\"California Housing Test Loss:\", loss1)\n",
    "\n",
    "loss2 = model2.evaluate(X2_test, y2_test)\n",
    "print(\"Diabetes Test Loss:\", loss2)\n",
    "\n",
    "# Plot training and validation loss for California Housing dataset\n",
    "plt.plot(history1.history['loss'], label='Housing Training Loss')\n",
    "plt.plot(history1.history['val_loss'], label='Housing Validation Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.title('California Housing Dataset')\n",
    "plt.show()\n",
    "\n",
    "# Plot training and validation loss for Diabetes dataset\n",
    "plt.plot(history2.history['loss'], label='Diabetes Training Loss')\n",
    "plt.plot(history2.history['val_loss'], label='Diabetes Validation Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.title('Diabetes Dataset')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67bfb0c6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "eeaeae0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   carat      cut color clarity  depth  table  price     x     y     z\n",
      "0   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
      "1   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
      "2   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
      "3   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
      "4   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75\n",
      "Epoch 1/10\n",
      "1079/1079 [==============================] - 3s 2ms/step - loss: 31336922.0000 - val_loss: 31768486.0000\n",
      "Epoch 2/10\n",
      "1079/1079 [==============================] - 2s 2ms/step - loss: 31298018.0000 - val_loss: 31728488.0000\n",
      "Epoch 3/10\n",
      "1079/1079 [==============================] - 2s 2ms/step - loss: 31259040.0000 - val_loss: 31688716.0000\n",
      "Epoch 4/10\n",
      "1079/1079 [==============================] - 2s 2ms/step - loss: 31219936.0000 - val_loss: 31648662.0000\n",
      "Epoch 5/10\n",
      "1079/1079 [==============================] - 2s 2ms/step - loss: 31180920.0000 - val_loss: 31608894.0000\n",
      "Epoch 6/10\n",
      "1079/1079 [==============================] - 2s 2ms/step - loss: 31142116.0000 - val_loss: 31569300.0000\n",
      "Epoch 7/10\n",
      "1079/1079 [==============================] - 3s 2ms/step - loss: 31103418.0000 - val_loss: 31529670.0000\n",
      "Epoch 8/10\n",
      "1079/1079 [==============================] - 2s 2ms/step - loss: 31064648.0000 - val_loss: 31490022.0000\n",
      "Epoch 9/10\n",
      "1079/1079 [==============================] - 2s 2ms/step - loss: 31025962.0000 - val_loss: 31450520.0000\n",
      "Epoch 10/10\n",
      "1079/1079 [==============================] - 2s 2ms/step - loss: 30987332.0000 - val_loss: 31411064.0000\n",
      "338/338 [==============================] - 1s 2ms/step - loss: 30768496.0000\n",
      "Test Loss: 30768496.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "338/338 [==============================] - 1s 1ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the Diamonds dataset\n",
    "diamonds = sns.load_dataset('diamonds')\n",
    "\n",
    "# Display the first few rows of the dataset\n",
    "print(diamonds.head())\n",
    "\n",
    "# Selecting features and target\n",
    "X = diamonds.drop(['price'], axis=1)\n",
    "y = diamonds['price'].values.reshape(-1, 1)\n",
    "\n",
    "# Convert categorical variables to numerical using one-hot encoding\n",
    "X_encoded = pd.get_dummies(X, columns=['cut', 'color', 'clarity'])\n",
    "\n",
    "# Splitting the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_encoded, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Standardize features\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# Define the model\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Input(shape=(X_train.shape[1],)),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)\n",
    "\n",
    "# Evaluate the model\n",
    "loss = model.evaluate(X_test, y_test)\n",
    "print(\"Test Loss:\", loss)\n",
    "\n",
    "# Plot training and validation loss\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')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Make predictions on test data\n",
    "predictions = model.predict(X_test)\n",
    "\n",
    "# Plot actual vs. predicted values with different colors\n",
    "plt.scatter(y_test, predictions, color='blue', label='Actual vs. Predicted')\n",
    "plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], color='red', linestyle='--', label='Perfect Prediction')\n",
    "plt.xlabel('Actual Prices')\n",
    "plt.ylabel('Predicted Prices')\n",
    "plt.title('Actual vs. Predicted Prices')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "29871345",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1/1 [==============================] - 1s 852ms/step - loss: 109117.5391 - val_loss: 109622.9922\n",
      "Epoch 2/100\n",
      "1/1 [==============================] - 0s 73ms/step - loss: 109114.0625 - val_loss: 109630.9297\n",
      "Epoch 3/100\n",
      "1/1 [==============================] - 0s 75ms/step - loss: 109110.5859 - val_loss: 109638.8750\n",
      "Epoch 4/100\n",
      "1/1 [==============================] - 0s 75ms/step - loss: 109107.1016 - val_loss: 109646.8203\n",
      "Epoch 5/100\n",
      "1/1 [==============================] - 0s 65ms/step - loss: 109103.6328 - val_loss: 109654.7578\n",
      "Epoch 6/100\n",
      "1/1 [==============================] - 0s 78ms/step - loss: 109100.1562 - val_loss: 109662.7031\n",
      "Epoch 7/100\n",
      "1/1 [==============================] - 0s 73ms/step - loss: 109096.6641 - val_loss: 109670.6484\n",
      "Epoch 8/100\n",
      "1/1 [==============================] - 0s 61ms/step - loss: 109093.1875 - val_loss: 109678.5938\n",
      "Epoch 9/100\n",
      "1/1 [==============================] - 0s 46ms/step - loss: 109089.7109 - val_loss: 109686.5312\n",
      "Epoch 10/100\n",
      "1/1 [==============================] - 0s 38ms/step - loss: 109086.2422 - val_loss: 109694.4609\n",
      "Epoch 11/100\n",
      "1/1 [==============================] - 0s 46ms/step - loss: 109082.7500 - val_loss: 109702.4062\n",
      "Epoch 12/100\n",
      "1/1 [==============================] - 0s 44ms/step - loss: 109079.2734 - val_loss: 109710.3516\n",
      "Epoch 13/100\n",
      "1/1 [==============================] - 0s 42ms/step - loss: 109075.7891 - val_loss: 109718.2969\n",
      "Epoch 14/100\n",
      "1/1 [==============================] - 0s 47ms/step - loss: 109072.3203 - val_loss: 109726.2422\n",
      "Epoch 15/100\n",
      "1/1 [==============================] - 0s 51ms/step - loss: 109068.8359 - val_loss: 109734.1875\n",
      "Epoch 16/100\n",
      "1/1 [==============================] - 0s 65ms/step - loss: 109065.3516 - val_loss: 109742.1328\n",
      "Epoch 17/100\n",
      "1/1 [==============================] - 0s 55ms/step - loss: 109061.8828 - val_loss: 109750.0781\n",
      "Epoch 18/100\n",
      "1/1 [==============================] - 0s 69ms/step - loss: 109058.4062 - val_loss: 109758.0000\n",
      "Epoch 19/100\n",
      "1/1 [==============================] - 0s 49ms/step - loss: 109054.9375 - val_loss: 109765.9531\n",
      "Epoch 20/100\n",
      "1/1 [==============================] - 0s 57ms/step - loss: 109051.4609 - val_loss: 109773.8984\n",
      "Epoch 21/100\n",
      "1/1 [==============================] - 0s 37ms/step - loss: 109047.9766 - val_loss: 109781.8438\n",
      "Epoch 22/100\n",
      "1/1 [==============================] - 0s 42ms/step - loss: 109044.5078 - val_loss: 109789.7891\n",
      "Epoch 23/100\n",
      "1/1 [==============================] - 0s 82ms/step - loss: 109041.0234 - val_loss: 109797.7422\n",
      "Epoch 24/100\n",
      "1/1 [==============================] - 0s 70ms/step - loss: 109037.5391 - val_loss: 109805.6719\n",
      "Epoch 25/100\n",
      "1/1 [==============================] - 0s 46ms/step - loss: 109034.0703 - val_loss: 109813.6172\n",
      "Epoch 26/100\n",
      "1/1 [==============================] - 0s 42ms/step - loss: 109030.6016 - val_loss: 109821.5703\n",
      "Epoch 27/100\n",
      "1/1 [==============================] - 0s 40ms/step - loss: 109027.1250 - val_loss: 109829.5156\n",
      "Epoch 28/100\n",
      "1/1 [==============================] - 0s 46ms/step - loss: 109023.6562 - val_loss: 109837.4688\n",
      "Epoch 29/100\n",
      "1/1 [==============================] - 0s 55ms/step - loss: 109020.1641 - val_loss: 109845.4141\n",
      "Epoch 30/100\n",
      "1/1 [==============================] - 0s 50ms/step - loss: 109016.6875 - val_loss: 109853.3438\n",
      "Epoch 31/100\n",
      "1/1 [==============================] - 0s 54ms/step - loss: 109013.2266 - val_loss: 109861.2969\n",
      "Epoch 32/100\n",
      "1/1 [==============================] - 0s 65ms/step - loss: 109009.7422 - val_loss: 109869.2500\n",
      "Epoch 33/100\n",
      "1/1 [==============================] - 0s 65ms/step - loss: 109006.2812 - val_loss: 109877.1953\n",
      "Epoch 34/100\n",
      "1/1 [==============================] - 0s 41ms/step - loss: 109002.7891 - val_loss: 109885.1250\n",
      "Epoch 35/100\n",
      "1/1 [==============================] - 0s 41ms/step - loss: 108999.3359 - val_loss: 109893.0781\n",
      "Epoch 36/100\n",
      "1/1 [==============================] - 0s 52ms/step - loss: 108995.8516 - val_loss: 109901.0312\n",
      "Epoch 37/100\n",
      "1/1 [==============================] - 0s 65ms/step - loss: 108992.3750 - val_loss: 109908.9844\n",
      "Epoch 38/100\n",
      "1/1 [==============================] - 0s 45ms/step - loss: 108988.8984 - val_loss: 109916.9141\n",
      "Epoch 39/100\n",
      "1/1 [==============================] - 0s 42ms/step - loss: 108985.4375 - val_loss: 109924.8672\n",
      "Epoch 40/100\n",
      "1/1 [==============================] - 0s 43ms/step - loss: 108981.9609 - val_loss: 109932.8203\n",
      "Epoch 41/100\n",
      "1/1 [==============================] - 0s 46ms/step - loss: 108978.4766 - val_loss: 109940.7734\n",
      "Epoch 42/100\n",
      "1/1 [==============================] - 0s 42ms/step - loss: 108975.0000 - val_loss: 109948.7109\n",
      "Epoch 43/100\n",
      "1/1 [==============================] - 0s 40ms/step - loss: 108971.5234 - val_loss: 109956.6641\n",
      "Epoch 44/100\n",
      "1/1 [==============================] - 0s 49ms/step - loss: 108968.0625 - val_loss: 109964.6172\n",
      "Epoch 45/100\n",
      "1/1 [==============================] - 0s 48ms/step - loss: 108964.5859 - val_loss: 109972.5469\n",
      "Epoch 46/100\n",
      "1/1 [==============================] - 0s 41ms/step - loss: 108961.1016 - val_loss: 109980.5078\n",
      "Epoch 47/100\n",
      "1/1 [==============================] - 0s 37ms/step - loss: 108957.6328 - val_loss: 109988.4609\n",
      "Epoch 48/100\n",
      "1/1 [==============================] - 0s 46ms/step - loss: 108954.1641 - val_loss: 109996.3906\n",
      "Epoch 49/100\n",
      "1/1 [==============================] - 0s 48ms/step - loss: 108950.6953 - val_loss: 110004.3516\n",
      "Epoch 50/100\n",
      "1/1 [==============================] - 0s 48ms/step - loss: 108947.2188 - val_loss: 110012.3047\n",
      "Epoch 51/100\n",
      "1/1 [==============================] - 0s 41ms/step - loss: 108943.7500 - val_loss: 110020.2422\n",
      "Epoch 52/100\n",
      "1/1 [==============================] - 0s 49ms/step - loss: 108940.2734 - val_loss: 110028.1953\n",
      "Epoch 53/100\n",
      "1/1 [==============================] - 0s 48ms/step - loss: 108936.8125 - val_loss: 110036.1328\n",
      "Epoch 54/100\n",
      "1/1 [==============================] - 0s 48ms/step - loss: 108933.3438 - val_loss: 110044.0938\n",
      "Epoch 55/100\n",
      "1/1 [==============================] - 0s 48ms/step - loss: 108929.8750 - val_loss: 110052.0469\n",
      "Epoch 56/100\n",
      "1/1 [==============================] - 0s 45ms/step - loss: 108926.3984 - val_loss: 110059.9844\n",
      "Epoch 57/100\n",
      "1/1 [==============================] - 0s 72ms/step - loss: 108922.9297 - val_loss: 110067.9453\n",
      "Epoch 58/100\n",
      "1/1 [==============================] - 0s 116ms/step - loss: 108919.4609 - val_loss: 110075.8828\n",
      "Epoch 59/100\n",
      "1/1 [==============================] - 0s 52ms/step - loss: 108915.9766 - val_loss: 110083.8438\n",
      "Epoch 60/100\n",
      "1/1 [==============================] - 0s 57ms/step - loss: 108912.5078 - val_loss: 110091.7969\n",
      "Epoch 61/100\n",
      "1/1 [==============================] - 0s 69ms/step - loss: 108909.0391 - val_loss: 110099.7344\n",
      "Epoch 62/100\n",
      "1/1 [==============================] - 0s 48ms/step - loss: 108905.5703 - val_loss: 110107.6953\n",
      "Epoch 63/100\n",
      "1/1 [==============================] - 0s 55ms/step - loss: 108902.1016 - val_loss: 110115.6328\n",
      "Epoch 64/100\n",
      "1/1 [==============================] - 0s 45ms/step - loss: 108898.6328 - val_loss: 110123.5938\n",
      "Epoch 65/100\n",
      "1/1 [==============================] - 0s 40ms/step - loss: 108895.1641 - val_loss: 110131.5391\n",
      "Epoch 66/100\n",
      "1/1 [==============================] - 0s 42ms/step - loss: 108891.6953 - val_loss: 110139.5000\n",
      "Epoch 67/100\n",
      "1/1 [==============================] - 0s 44ms/step - loss: 108888.2266 - val_loss: 110147.4375\n",
      "Epoch 68/100\n",
      "1/1 [==============================] - 0s 49ms/step - loss: 108884.7578 - val_loss: 110155.3984\n",
      "Epoch 69/100\n",
      "1/1 [==============================] - 0s 60ms/step - loss: 108881.2891 - val_loss: 110163.3359\n",
      "Epoch 70/100\n",
      "1/1 [==============================] - 0s 60ms/step - loss: 108877.8125 - val_loss: 110171.2969\n",
      "Epoch 71/100\n",
      "1/1 [==============================] - 0s 49ms/step - loss: 108874.3516 - val_loss: 110179.2422\n",
      "Epoch 72/100\n",
      "1/1 [==============================] - 0s 51ms/step - loss: 108870.8750 - val_loss: 110187.2031\n",
      "Epoch 73/100\n",
      "1/1 [==============================] - 0s 54ms/step - loss: 108867.4141 - val_loss: 110195.1484\n",
      "Epoch 74/100\n",
      "1/1 [==============================] - 0s 50ms/step - loss: 108863.9453 - val_loss: 110203.1094\n",
      "Epoch 75/100\n",
      "1/1 [==============================] - 0s 50ms/step - loss: 108860.4766 - val_loss: 110211.0547\n",
      "Epoch 76/100\n",
      "1/1 [==============================] - 0s 53ms/step - loss: 108857.0078 - val_loss: 110219.0156\n",
      "Epoch 77/100\n",
      "1/1 [==============================] - 0s 46ms/step - loss: 108853.5391 - val_loss: 110226.9609\n",
      "Epoch 78/100\n",
      "1/1 [==============================] - 0s 41ms/step - loss: 108850.0859 - val_loss: 110234.8984\n",
      "Epoch 79/100\n",
      "1/1 [==============================] - 0s 53ms/step - loss: 108846.6172 - val_loss: 110242.8672\n",
      "Epoch 80/100\n",
      "1/1 [==============================] - 0s 49ms/step - loss: 108843.1484 - val_loss: 110250.8125\n",
      "Epoch 81/100\n",
      "1/1 [==============================] - 0s 50ms/step - loss: 108839.6797 - val_loss: 110258.7734\n",
      "Epoch 82/100\n",
      "1/1 [==============================] - 0s 48ms/step - loss: 108836.2109 - val_loss: 110266.7188\n",
      "Epoch 83/100\n",
      "1/1 [==============================] - 0s 49ms/step - loss: 108832.7500 - val_loss: 110274.6641\n",
      "Epoch 84/100\n",
      "1/1 [==============================] - 0s 47ms/step - loss: 108829.2734 - val_loss: 110282.6328\n",
      "Epoch 85/100\n",
      "1/1 [==============================] - 0s 51ms/step - loss: 108825.8125 - val_loss: 110290.5781\n",
      "Epoch 86/100\n",
      "1/1 [==============================] - 0s 48ms/step - loss: 108822.3438 - val_loss: 110298.5391\n",
      "Epoch 87/100\n",
      "1/1 [==============================] - 0s 51ms/step - loss: 108818.8750 - val_loss: 110306.4922\n",
      "Epoch 88/100\n",
      "1/1 [==============================] - 0s 47ms/step - loss: 108815.4141 - val_loss: 110314.4375\n",
      "Epoch 89/100\n",
      "1/1 [==============================] - 0s 51ms/step - loss: 108811.9453 - val_loss: 110322.3984\n",
      "Epoch 90/100\n",
      "1/1 [==============================] - 0s 49ms/step - loss: 108808.4922 - val_loss: 110330.3516\n",
      "Epoch 91/100\n",
      "1/1 [==============================] - 0s 46ms/step - loss: 108805.0234 - val_loss: 110338.2969\n",
      "Epoch 92/100\n",
      "1/1 [==============================] - 0s 44ms/step - loss: 108801.5625 - val_loss: 110346.2656\n",
      "Epoch 93/100\n",
      "1/1 [==============================] - 0s 56ms/step - loss: 108798.0859 - val_loss: 110354.2109\n",
      "Epoch 94/100\n",
      "1/1 [==============================] - 0s 68ms/step - loss: 108794.6250 - val_loss: 110362.1641\n",
      "Epoch 95/100\n",
      "1/1 [==============================] - 0s 73ms/step - loss: 108791.1641 - val_loss: 110370.1094\n",
      "Epoch 96/100\n",
      "1/1 [==============================] - 0s 71ms/step - loss: 108787.6953 - val_loss: 110378.0781\n",
      "Epoch 97/100\n",
      "1/1 [==============================] - 0s 65ms/step - loss: 108784.2422 - val_loss: 110386.0234\n",
      "Epoch 98/100\n",
      "1/1 [==============================] - 0s 60ms/step - loss: 108780.7734 - val_loss: 110393.9766\n",
      "Epoch 99/100\n",
      "1/1 [==============================] - 0s 49ms/step - loss: 108777.3125 - val_loss: 110401.9297\n",
      "Epoch 100/100\n",
      "1/1 [==============================] - 0s 48ms/step - loss: 108773.8516 - val_loss: 110409.8984\n",
      "1/1 [==============================] - 0s 52ms/step - loss: 105334.3203\n",
      "Test Loss: 105334.3203125\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 72ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the new dataset\n",
    "data = {\n",
    "    'carat': [0.23, 0.21, 0.23, 0.29, 0.31],\n",
    "    'cut': ['Ideal', 'Premium', 'Good', 'Premium', 'Good'],\n",
    "    'color': ['E', 'E', 'E', 'I', 'J'],\n",
    "    'clarity': ['SI2', 'SI1', 'VS1', 'VS2', 'SI2'],\n",
    "    'depth': [61.5, 59.8, 56.9, 62.4, 63.3],\n",
    "    'table': [55.0, 61.0, 65.0, 58.0, 58.0],\n",
    "    'x': [3.95, 3.89, 4.05, 4.20, 4.34],\n",
    "    'y': [3.98, 3.84, 4.07, 4.23, 4.35],\n",
    "    'z': [2.43, 2.31, 2.31, 2.63, 2.75],\n",
    "    'price': [326, 326, 327, 334, 335]\n",
    "}\n",
    "diamonds_df = pd.DataFrame(data)\n",
    "\n",
    "# Convert categorical variables to numerical using one-hot encoding\n",
    "diamonds_encoded = pd.get_dummies(diamonds_df, columns=['cut', 'color', 'clarity'])\n",
    "\n",
    "# Select features and target\n",
    "X = diamonds_encoded.drop('price', axis=1)\n",
    "y = diamonds_encoded['price'].values.reshape(-1, 1)\n",
    "\n",
    "# Splitting the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Standardize features\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Define the model\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Input(shape=(X_train_scaled.shape[1],)),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(X_train_scaled, y_train, epochs=100, batch_size=32, validation_split=0.2)\n",
    "\n",
    "# Evaluate the model\n",
    "loss = model.evaluate(X_test_scaled, y_test)\n",
    "print(\"Test Loss:\", loss)\n",
    "\n",
    "# Plot training and validation loss\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')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Make predictions on test data\n",
    "predictions = model.predict(X_test_scaled)\n",
    "\n",
    "# Plot actual vs. predicted values\n",
    "plt.scatter(y_test, predictions)\n",
    "plt.xlabel('Actual Prices')\n",
    "plt.ylabel('Predicted Prices')\n",
    "plt.title('Actual vs. Predicted Prices')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ab006b89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1079/1079 [==============================] - 3s 3ms/step - loss: 31327264.0000 - val_loss: 31758178.0000\n",
      "Epoch 2/10\n",
      "1079/1079 [==============================] - 2s 2ms/step - loss: 31288006.0000 - val_loss: 31718292.0000\n",
      "Epoch 3/10\n",
      "1079/1079 [==============================] - 2s 2ms/step - loss: 31248996.0000 - val_loss: 31678344.0000\n",
      "Epoch 4/10\n",
      "1079/1079 [==============================] - 3s 2ms/step - loss: 31210036.0000 - val_loss: 31638512.0000\n",
      "Epoch 5/10\n",
      "1079/1079 [==============================] - 2s 2ms/step - loss: 31171230.0000 - val_loss: 31598966.0000\n",
      "Epoch 6/10\n",
      "1079/1079 [==============================] - 2s 2ms/step - loss: 31132340.0000 - val_loss: 31559074.0000\n",
      "Epoch 7/10\n",
      "1079/1079 [==============================] - 3s 2ms/step - loss: 31093472.0000 - val_loss: 31519462.0000\n",
      "Epoch 8/10\n",
      "1079/1079 [==============================] - 3s 2ms/step - loss: 31054746.0000 - val_loss: 31479864.0000\n",
      "Epoch 9/10\n",
      "1079/1079 [==============================] - 3s 2ms/step - loss: 31016028.0000 - val_loss: 31440228.0000\n",
      "Epoch 10/10\n",
      "1079/1079 [==============================] - 2s 2ms/step - loss: 30977398.0000 - val_loss: 31400886.0000\n",
      "338/338 [==============================] - 1s 2ms/step - loss: 30758120.0000\n",
      "Test Loss: 30758120.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "338/338 [==============================] - 1s 2ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the Diamonds dataset\n",
    "diamonds = sns.load_dataset('diamonds')\n",
    "\n",
    "# Selecting features and target\n",
    "X = diamonds.drop('price', axis=1)\n",
    "y = diamonds['price'].values.reshape(-1, 1)\n",
    "\n",
    "# Convert categorical variables to numerical using one-hot encoding\n",
    "X_encoded = pd.get_dummies(X, columns=['cut', 'color', 'clarity'])\n",
    "\n",
    "# Splitting the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_encoded, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Standardize features\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Define the model\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Input(shape=(X_train_scaled.shape[1],)),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(X_train_scaled, y_train, epochs=10, batch_size=32, validation_split=0.2)\n",
    "\n",
    "# Evaluate the model\n",
    "loss = model.evaluate(X_test_scaled, y_test)\n",
    "print(\"Test Loss:\", loss)\n",
    "\n",
    "# Plot training and validation loss\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')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Make predictions on test data\n",
    "predictions = model.predict(X_test_scaled)\n",
    "\n",
    "# Plot actual vs. predicted values\n",
    "plt.scatter(y_test, predictions)\n",
    "plt.xlabel('Actual Prices')\n",
    "plt.ylabel('Predicted Prices')\n",
    "plt.title('Actual vs. Predicted Prices')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7222424c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "912e16eb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}