VEHICLE LIVE RISK PREDICTION

Vehicle Live Risk Prediction/Model/ANN_Based.ipynb

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+{
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+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c26d8b33-9032-4972-91cb-70eedea7591d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.metrics import accuracy_score, classification_report\n",
+    "from tensorflow import keras\n",
+    "from tensorflow.keras import layers\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "df = pd.read_csv(\"Downloads/Vehicle Risk Prediction Dataset.csv\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Encode categorical features using LabelEncoder\n",
+    "le_visibility = LabelEncoder()\n",
+    "le_road_surface_conditions = LabelEncoder()\n",
+    "le_weather = LabelEncoder()\n",
+    "le_traffic_density = LabelEncoder()\n",
+    "le_road_hazards = LabelEncoder()\n",
+    "#le_time_of_day = LabelEncoder()\n",
+    "le_fatigue_level = LabelEncoder()\n",
+    "le_medical_condition = LabelEncoder()\n",
+    "le_speeding = LabelEncoder()\n",
+    "le_light= LabelEncoder()\n",
+    "#le_road_type=LabelEncoder()\n",
+    "#le_landscape=LabelEncoder()\n",
+    "\n",
+    "df['visibility_n'] = le_visibility.fit_transform(df['Visibility'])\n",
+    "df['road_surface_conditions_n'] = le_road_surface_conditions.fit_transform(df['Road_Surface_Conditions'])\n",
+    "df['weather_n'] = le_weather.fit_transform(df['Weather'])\n",
+    "df['traffic_density_n'] = le_traffic_density.fit_transform(df['Traffic_Density'])\n",
+    "df['road_hazards_n'] = le_road_hazards.fit_transform(df['Road_Hazards'])\n",
+    "#df['time_of_day_n'] = le_time_of_day.fit_transform(df['Time_of_Day'])\n",
+    "df['fatigue_level_n'] = le_fatigue_level.fit_transform(df['Fatigue_Level'])\n",
+    "df['medical_condition_n'] = le_medical_condition.fit_transform(df['Medical_Condition'])\n",
+    "df['speeding_n'] = le_speeding.fit_transform(df['Speeding'])\n",
+    "df['light_condition']=le_light.fit_transform(df['Light_Conditions'])\n",
+    "#df['roadtype'] = le_road_type.fit_transform(df['Road_Type'])\n",
+    "#df['landscape_n']=le_landscape.fit_transform(df['Landscape'])\n",
+    "\n",
+    "df = df.drop(['Light_Conditions', 'Road_Type', 'Landscape', 'Visibility', 'Road_Surface_Conditions', 'Weather', 'Traffic_Density', 'Road_Hazards', 'Time_of_Day', 'Fatigue_Level', 'Medical_Condition', 'Speeding','Driver_Age','Last_Service_Months_Ago'], axis='columns')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a355b768-dc55-4b75-a35a-9b7125797e72",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-01-16 00:23:27.443768: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/2\n",
+      "1920/1920 [==============================] - 7s 3ms/step - loss: 0.0174 - accuracy: 0.9957 - val_loss: 0.0157 - val_accuracy: 0.9983\n",
+      "Epoch 2/2\n",
+      "1920/1920 [==============================] - 6s 3ms/step - loss: 4.1210e-04 - accuracy: 0.9999 - val_loss: 0.0180 - val_accuracy: 0.9983\n",
+      "94/94 [==============================] - 0s 2ms/step\n",
+      "Accuracy: 0.9996666666666667\n",
+      "\n",
+      "Classification Report:\n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00      1168\n",
+      "           1       1.00      1.00      1.00      1832\n",
+      "\n",
+      "    accuracy                           1.00      3000\n",
+      "   macro avg       1.00      1.00      1.00      3000\n",
+      "weighted avg       1.00      1.00      1.00      3000\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "X= df.drop('Risk_Score', axis=1)\n",
+    "y = df['Risk_Score'].apply(lambda x: 1 if x > 50 else 0)\n",
+    "\n",
+    "# Split the data 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 (important for neural networks)\n",
+    "scaler = StandardScaler()\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)\n",
+    "\n",
+    "# Create an Artificial Neural Network model with dropout layers\n",
+    "model = keras.Sequential([\n",
+    "    layers.Dense(64, activation='relu', input_dim=X_train.shape[1]),\n",
+    "    layers.Dropout(0.2),  # Add dropout layer\n",
+    "    layers.Dense(32, activation='relu'),\n",
+    "    layers.Dropout(0.2),  # Add dropout layer\n",
+    "    layers.Dense(1, activation='sigmoid')  # For binary classification, sigmoid activation\n",
+    "])\n",
+    "\n",
+    "# Compile the model\n",
+    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
+    "\n",
+    "# Train the model\n",
+    "model.fit(X_train_scaled, y_train, epochs=2, batch_size=5, validation_split=0.2)\n",
+    "\n",
+    "# Make predictions on the test set\n",
+    "y_pred_proba = model.predict(X_test_scaled)\n",
+    "y_pred = (y_pred_proba > 0.5).astype(int)\n",
+    "\n",
+    "# Evaluate the model\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "class_report = classification_report(y_test, y_pred)\n",
+    "\n",
+    "print(\"Accuracy:\", accuracy)\n",
+    "print(\"\\nClassification Report:\\n\", class_report)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d165f77a-a6c5-41a1-8fb7-bb95b6e481a1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
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