diff --git a/Decceleration_analysisof_athelete_using_LSTM_real_world_project6.ipynb b/Decceleration_analysisof_athelete_using_LSTM_real_world_project6.ipynb
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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyMGBVGVDEn8nFHYnHzp0U31",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Orrm23/DeepSeek-Coder/blob/main/Decceleration_analysisof_athelete_using_LSTM_real_world_project6.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import tensorflow as tf\n",
+        "import matplotlib.pyplot as plt\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import LSTM, Dense\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "\n",
+        "# Load dataset from CSV file\n",
+        "def load_rpm_data(file_path):\n",
+        "    df = pd.read_csv('rpm_deceleration_dataset.csv')\n",
+        "    X = df.iloc[:, :-1].values  # Features (RPM data sequences)\n",
+        "    y = df.iloc[:, -1].values   # Labels (1 for deceleration, 0 otherwise)\n",
+        "    return X, y\n",
+        "\n",
+        "# File path to dataset\n",
+        "file_path = r\"E:\\Decceleration_analysisof_athelete using LSTM real world project6\\rpm_deceleration_dataset.csv\"\n",
+        "\n",
+        "# Prepare dataset\n",
+        "X, y = load_rpm_data(file_path)\n",
+        "scaler = StandardScaler()\n",
+        "X = scaler.fit_transform(X)\n",
+        "X = X.reshape(X.shape[0], X.shape[1], 1)  # Reshape for LSTM\n",
+        "\n",
+        "# Train-test split\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+        "\n",
+        "# Define LSTM model\n",
+        "model = Sequential([\n",
+        "    LSTM(50, return_sequences=False, input_shape=(X_train.shape[1], 1)),\n",
+        "    Dense(1, activation='sigmoid')\n",
+        "])\n",
+        "\n",
+        "# Compile model\n",
+        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
+        "\n",
+        "# Train model\n",
+        "model.fit(X_train, y_train, epochs=20, batch_size=16, validation_data=(X_test, y_test))\n",
+        "\n",
+        "# Evaluate model\n",
+        "test_loss, test_accuracy = model.evaluate(X_test, y_test)\n",
+        "print(f\"Test Accuracy: {test_accuracy * 100:.2f}%\")\n",
+        "\n",
+        "# Plot histogram for deceleration per minute\n",
+        "plt.hist(y, bins=10, edgecolor='black')\n",
+        "plt.xlabel(\"Deceleration per Minute\")\n",
+        "plt.ylabel(\"Frequency\")\n",
+        "plt.title(\"Histogram of Deceleration per Minute\")\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "UUZslFlZkBpN",
+        "outputId": "bc7782c9-d45b-4f0f-bdf5-51f021355a4d"
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/20\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/rnn/rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+            "  super().__init__(**kwargs)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 8ms/step - accuracy: 0.6656 - loss: 0.6481 - val_accuracy: 0.6670 - val_loss: 0.6271\n",
+            "Epoch 2/20\n",
+            "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.6743 - loss: 0.6228 - val_accuracy: 0.6670 - val_loss: 0.6166\n",
+            "Epoch 3/20\n",
+            "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.6601 - loss: 0.6213 - val_accuracy: 0.6820 - val_loss: 0.5975\n",
+            "Epoch 4/20\n",
+            "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.6579 - loss: 0.6192 - val_accuracy: 0.6800 - val_loss: 0.5720\n",
+            "Epoch 5/20\n",
+            "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.6803 - loss: 0.5896 - val_accuracy: 0.6830 - val_loss: 0.5519\n",
+            "Epoch 6/20\n",
+            "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.6937 - loss: 0.5628 - val_accuracy: 0.6500 - val_loss: 0.5887\n",
+            "Epoch 7/20\n",
+            "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.7021 - loss: 0.5405 - val_accuracy: 0.7390 - val_loss: 0.4979\n",
+            "Epoch 8/20\n",
+            "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.7465 - loss: 0.4933 - val_accuracy: 0.7320 - val_loss: 0.5215\n",
+            "Epoch 9/20\n",
+            "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.7660 - loss: 0.4742 - val_accuracy: 0.7850 - val_loss: 0.4273\n",
+            "Epoch 10/20\n",
+            "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.8209 - loss: 0.3972 - val_accuracy: 0.8270 - val_loss: 0.3901\n",
+            "Epoch 11/20\n",
+            "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.8213 - loss: 0.3796 - val_accuracy: 0.8420 - val_loss: 0.3595\n",
+            "Epoch 12/20\n",
+            "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.8408 - loss: 0.3634 - val_accuracy: 0.7950 - val_loss: 0.4218\n",
+            "Epoch 13/20\n",
+            "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 9ms/step - accuracy: 0.8401 - loss: 0.3503 - val_accuracy: 0.8330 - val_loss: 0.3686\n",
+            "Epoch 14/20\n",
+            "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.8424 - loss: 0.3528 - val_accuracy: 0.8220 - val_loss: 0.3770\n",
+            "Epoch 15/20\n",
+            "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.8564 - loss: 0.3298 - val_accuracy: 0.8210 - val_loss: 0.4038\n",
+            "Epoch 16/20\n",
+            "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step - accuracy: 0.8446 - loss: 0.3439 - val_accuracy: 0.8730 - val_loss: 0.3035\n",
+            "Epoch 17/20\n",
+            "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - accuracy: 0.8501 - loss: 0.3258 - val_accuracy: 0.8690 - val_loss: 0.3123\n",
+            "Epoch 18/20\n",
+            "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - accuracy: 0.8560 - loss: 0.3279 - val_accuracy: 0.8600 - val_loss: 0.3087\n",
+            "Epoch 19/20\n",
+            "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.8530 - loss: 0.3319 - val_accuracy: 0.8550 - val_loss: 0.3229\n",
+            "Epoch 20/20\n",
+            "\u001b[1m250/250\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 7ms/step - accuracy: 0.8545 - loss: 0.3269 - val_accuracy: 0.8500 - val_loss: 0.3279\n",
+            "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8555 - loss: 0.3230\n",
+            "Test Accuracy: 85.00%\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file