neuralnetwork_capacity_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "28b60f9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Laptech\\AppData\\Local\\Temp\\ipykernel_12320\\311431551.py:1: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From d:\\Python\\Python311\\Lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "66232c38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(524, 51)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_file_path = 'input(50)=3.2-3.6 ahakku , output(1)=4.2 ahakku train.csv'\n",
    "\n",
    "df = pd.read_csv(csv_file_path)\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1d4bc1d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:, :-1]\n",
    "y = df.iloc[:, -1:]\n",
    "\n",
    "# y = y*100\n",
    "\n",
    "X_train =X\n",
    "y_train =y\n",
    "\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)\n",
    "\n",
    "# # Print the shapes of the resulting sets\n",
    "# print(\"X_train shape:\", X_train.shape)\n",
    "# print(\"X_test shape:\", X_test.shape)\n",
    "# print(\"y_train shape:\", y_train.shape)\n",
    "# print(\"y_test shape:\", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bc52db3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_4 (Dense)             (None, 64)                3264      \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 128)               8320      \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 64)                8256      \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 19905 (77.75 KB)\n",
      "Trainable params: 19905 (77.75 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "    layers.Dense(64, activation='relu', input_shape=(X_train.shape[1],)),\n",
    "    layers.Dense(128, activation='relu'),\n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dense(1, activation='linear')\n",
    "])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bd70938f",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss=tf.compat.v1.losses.mean_squared_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3e136f79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "11/11 [==============================] - 3s 89ms/step - loss: 1.1409 - val_loss: 0.3178\n",
      "Epoch 2/50\n",
      "11/11 [==============================] - 0s 21ms/step - loss: 0.2244 - val_loss: 0.2061\n",
      "Epoch 3/50\n",
      "11/11 [==============================] - 0s 23ms/step - loss: 0.1473 - val_loss: 0.1208\n",
      "Epoch 4/50\n",
      "11/11 [==============================] - 0s 18ms/step - loss: 0.1161 - val_loss: 0.0802\n",
      "Epoch 5/50\n",
      "11/11 [==============================] - 0s 22ms/step - loss: 0.0865 - val_loss: 0.0556\n",
      "Epoch 6/50\n",
      "11/11 [==============================] - 0s 23ms/step - loss: 0.0640 - val_loss: 0.0349\n",
      "Epoch 7/50\n",
      "11/11 [==============================] - 0s 19ms/step - loss: 0.0421 - val_loss: 0.0186\n",
      "Epoch 8/50\n",
      "11/11 [==============================] - 0s 23ms/step - loss: 0.0265 - val_loss: 0.0107\n",
      "Epoch 9/50\n",
      "11/11 [==============================] - 0s 24ms/step - loss: 0.0195 - val_loss: 0.0088\n",
      "Epoch 10/50\n",
      "11/11 [==============================] - 0s 20ms/step - loss: 0.0161 - val_loss: 0.0074\n",
      "Epoch 11/50\n",
      "11/11 [==============================] - 0s 22ms/step - loss: 0.0134 - val_loss: 0.0047\n",
      "Epoch 12/50\n",
      "11/11 [==============================] - 0s 24ms/step - loss: 0.0112 - val_loss: 0.0039\n",
      "Epoch 13/50\n",
      "11/11 [==============================] - 0s 26ms/step - loss: 0.0111 - val_loss: 0.0031\n",
      "Epoch 14/50\n",
      "11/11 [==============================] - 0s 23ms/step - loss: 0.0091 - val_loss: 0.0028\n",
      "Epoch 15/50\n",
      "11/11 [==============================] - 0s 22ms/step - loss: 0.0082 - val_loss: 0.0024\n",
      "Epoch 16/50\n",
      "11/11 [==============================] - 0s 20ms/step - loss: 0.0080 - val_loss: 0.0021\n",
      "Epoch 17/50\n",
      "11/11 [==============================] - 0s 21ms/step - loss: 0.0076 - val_loss: 0.0018\n",
      "Epoch 18/50\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0074 - val_loss: 0.0020\n",
      "Epoch 19/50\n",
      "11/11 [==============================] - 0s 18ms/step - loss: 0.0073 - val_loss: 0.0019\n",
      "Epoch 20/50\n",
      "11/11 [==============================] - 0s 15ms/step - loss: 0.0073 - val_loss: 0.0024\n",
      "Epoch 21/50\n",
      "11/11 [==============================] - 0s 12ms/step - loss: 0.0088 - val_loss: 0.0027\n",
      "Epoch 22/50\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0073 - val_loss: 0.0018\n",
      "Epoch 23/50\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 0.0076 - val_loss: 0.0029\n",
      "Epoch 24/50\n",
      "11/11 [==============================] - 0s 14ms/step - loss: 0.0078 - val_loss: 0.0021\n",
      "Epoch 25/50\n",
      "11/11 [==============================] - 0s 17ms/step - loss: 0.0078 - val_loss: 0.0020\n",
      "Epoch 26/50\n",
      "11/11 [==============================] - 0s 23ms/step - loss: 0.0074 - val_loss: 0.0025\n",
      "Epoch 27/50\n",
      "11/11 [==============================] - 0s 35ms/step - loss: 0.0073 - val_loss: 0.0019\n",
      "Epoch 28/50\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 0.0074 - val_loss: 0.0018\n",
      "Epoch 29/50\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 0.0074 - val_loss: 0.0040\n",
      "Epoch 30/50\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 0.0098 - val_loss: 0.0110\n",
      "Epoch 31/50\n",
      "11/11 [==============================] - 0s 15ms/step - loss: 0.0105 - val_loss: 0.0033\n",
      "Epoch 32/50\n",
      "11/11 [==============================] - 0s 23ms/step - loss: 0.0086 - val_loss: 0.0017\n",
      "Epoch 33/50\n",
      "11/11 [==============================] - 0s 23ms/step - loss: 0.0076 - val_loss: 0.0020\n",
      "Epoch 34/50\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 0.0081 - val_loss: 0.0022\n",
      "Epoch 35/50\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 0.0075 - val_loss: 0.0058\n",
      "Epoch 36/50\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 0.0083 - val_loss: 0.0015\n",
      "Epoch 37/50\n",
      "11/11 [==============================] - 0s 17ms/step - loss: 0.0072 - val_loss: 0.0022\n",
      "Epoch 38/50\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 0.0071 - val_loss: 0.0018\n",
      "Epoch 39/50\n",
      "11/11 [==============================] - 0s 12ms/step - loss: 0.0078 - val_loss: 0.0019\n",
      "Epoch 40/50\n",
      "11/11 [==============================] - 0s 12ms/step - loss: 0.0076 - val_loss: 0.0023\n",
      "Epoch 41/50\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 0.0077 - val_loss: 0.0036\n",
      "Epoch 42/50\n",
      "11/11 [==============================] - 0s 11ms/step - loss: 0.0073 - val_loss: 0.0021\n",
      "Epoch 43/50\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 0.0072 - val_loss: 0.0019\n",
      "Epoch 44/50\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 0.0076 - val_loss: 0.0018\n",
      "Epoch 45/50\n",
      "11/11 [==============================] - 0s 8ms/step - loss: 0.0075 - val_loss: 0.0019\n",
      "Epoch 46/50\n",
      "11/11 [==============================] - 0s 9ms/step - loss: 0.0077 - val_loss: 0.0029\n",
      "Epoch 47/50\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 0.0078 - val_loss: 0.0024\n",
      "Epoch 48/50\n",
      "11/11 [==============================] - 0s 10ms/step - loss: 0.0074 - val_loss: 0.0025\n",
      "Epoch 49/50\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 0.0072 - val_loss: 0.0021\n",
      "Epoch 50/50\n",
      "11/11 [==============================] - 0s 7ms/step - loss: 0.0076 - val_loss: 0.0017\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, epochs=50, batch_size=40, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ff7545ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = np.arange(1, len(train_loss) + 1)\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(epochs, train_loss, label='Training Loss')\n",
    "plt.plot(epochs, val_loss, label='Validation Loss')\n",
    "plt.title('Training and Validation Loss Over Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0d116a66",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Evaluate the model on the test set\n",
    "# loss = model.evaluate(X_test, y_test)\n",
    "# print(f\"Test Loss: {loss}\")\n",
    "# accuracy = 1 - loss\n",
    "# print(f\"Accuracy: {accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5527ef78",
   "metadata": {},
   "outputs": [],
   "source": [
    "# predictions = model.predict(X_test)\n",
    "\n",
    "# r2 = r2_score(y_test, predictions)\n",
    "# print(f\"R-squared (R2) Score: {r2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4494f98e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# y_test_values = y_test.to_numpy().flatten()\n",
    "\n",
    "# predictions = model.predict(X_test).flatten()\n",
    "\n",
    "# differences = y_test_values - predictions\n",
    "\n",
    "# sns.histplot(differences, bins=20, kde=True, color='blue', edgecolor='black', alpha=0.7)\n",
    "# plt.xlabel('Difference (True Value - Predicted Value)')\n",
    "# plt.ylabel('Frequency')\n",
    "# plt.title('Distribution of Differences between True Values and Predictions with KDE')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c37a719",
   "metadata": {},
   "source": [
    "#### Plot actual values vs predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# predictions = model.predict(X_test).flatten()\n",
    "# plt.scatter(y_test, predictions,marker='.')\n",
    "# plt.xlabel('Actual Values')\n",
    "# plt.ylabel('Predictions')\n",
    "# plt.title('Actual Values vs Predictions')\n",
    "\n",
    "# plt.xlim(0.8, 1.15)\n",
    "# plt.ylim(0.8, 1.15)\n",
    "\n",
    "# plt.plot([0,2], [0,2], color='red', linestyle='--', label='Slope 1 Line')\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "08e8830a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(125, 51)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv_file_path = 'input(50)=3.2-3.6 ahakku , output(1)=4.2 ahakku test.csv'\n",
    "\n",
    "df = pd.read_csv(csv_file_path)\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "465bd738",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_ = df.iloc[:, :-1]\n",
    "y_test_ = df.iloc[:, -1:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d6dbbe74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0036\n",
      "Test Loss: 0.0035522463731467724\n",
      "Accuracy: 0.9964477536268532\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the test set\n",
    "loss = model.evaluate(X_test_, y_test_)\n",
    "print(f\"Test Loss: {loss}\")\n",
    "accuracy = 1 - loss\n",
    "print(f\"Accuracy: {accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "38860d05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 6ms/step\n",
      "R-squared (R2) Score: 0.9848798389984275\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(X_test_)\n",
    "\n",
    "r2 = r2_score(y_test_, predictions)\n",
    "print(f\"R-squared (R2) Score: {r2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "50505a1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 2ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = model.predict(X_test_).flatten()\n",
    "plt.scatter(y_test_, predictions,marker='.')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predictions')\n",
    "plt.title('Actual Values vs Predictions')\n",
    "\n",
    "# plt.xlim(0.8, 1.15)\n",
    "# plt.ylim(0.8, 1.15)\n",
    "\n",
    "plt.plot([0,2], [0,2], color='red', linestyle='--', label='Slope 1 Line')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2331f8bf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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