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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense\n",
"from scipy.stats import f\n",
"\n",
"# Load the dataset\n",
"dataset = pd.read_csv('/Users/ashishpoudel/Downloads/AircraftFuelPrediction-main/datasets/preprocessed_data.csv')\n",
"dataset.dropna(inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Features and target\n",
"features = dataset[['distance', 'model', 'seats', 'fuel_burn', 'fuel_burn_total']]\n",
"target = dataset['fuel_burn_total']\n",
"\n",
"# Encoding the 'model' column\n",
"encoder = OneHotEncoder(sparse_output=False)\n",
"model_encoded = pd.DataFrame(encoder.fit_transform(features[['model']]))\n",
"model_encoded.columns = encoder.get_feature_names_out(['model'])\n",
"\n",
"# Drop the original 'model' column and add the encoded data\n",
"features = features.drop('model', axis=1)\n",
"features = pd.concat([features.reset_index(drop=True), model_encoded.reset_index(drop=True)], axis=1)\n",
"\n",
"# Train-test split\n",
"feature_train, feature_test, target_train, target_test = train_test_split(features, target, test_size=0.1, random_state=42)\n",
"\n",
"# Feature scaling\n",
"scaler = StandardScaler()\n",
"feature_train_scaled = scaler.fit_transform(feature_train)\n",
"feature_test_scaled = scaler.transform(feature_test)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/50\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/anaconda3/envs/Intenv/lib/python3.9/site-packages/keras/src/layers/core/dense.py:87: 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__(activity_regularizer=activity_regularizer, **kwargs)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 6ms/step - loss: 140.5811\n",
"Epoch 2/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 1.9729\n",
"Epoch 3/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.7662\n",
"Epoch 4/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 7ms/step - loss: 0.8330\n",
"Epoch 5/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.7197\n",
"Epoch 6/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.7294\n",
"Epoch 7/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.6337\n",
"Epoch 8/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 7ms/step - loss: 0.4558\n",
"Epoch 9/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 7ms/step - loss: 0.3461\n",
"Epoch 10/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.4073\n",
"Epoch 11/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.3993\n",
"Epoch 12/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.3657\n",
"Epoch 13/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.3334\n",
"Epoch 14/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.3895\n",
"Epoch 15/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.4462\n",
"Epoch 16/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.2150\n",
"Epoch 17/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.3340\n",
"Epoch 18/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.2634\n",
"Epoch 19/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 7ms/step - loss: 0.2737\n",
"Epoch 20/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.2614\n",
"Epoch 21/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.2445\n",
"Epoch 22/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.2159\n",
"Epoch 23/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.4048\n",
"Epoch 24/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.2998\n",
"Epoch 25/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.2747\n",
"Epoch 26/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.2207\n",
"Epoch 27/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.1944\n",
"Epoch 28/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.3801\n",
"Epoch 29/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.2268\n",
"Epoch 30/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 6ms/step - loss: 0.2105\n",
"Epoch 31/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.1308\n",
"Epoch 32/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 6ms/step - loss: 0.1518\n",
"Epoch 33/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 6ms/step - loss: 0.1473\n",
"Epoch 34/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.2194\n",
"Epoch 35/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 6ms/step - loss: 0.1172\n",
"Epoch 36/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.1910\n",
"Epoch 37/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.1921\n",
"Epoch 38/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.2753\n",
"Epoch 39/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.2847\n",
"Epoch 40/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.1538\n",
"Epoch 41/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.1008\n",
"Epoch 42/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.1592\n",
"Epoch 43/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.0971\n",
"Epoch 44/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.1211\n",
"Epoch 45/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.1177\n",
"Epoch 46/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.0955\n",
"Epoch 47/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.0695\n",
"Epoch 48/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.2184\n",
"Epoch 49/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.1073\n",
"Epoch 50/50\n",
"\u001b[1m1314/1314\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 6ms/step - loss: 0.1462\n",
"\u001b[1m146/146\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0717\n",
"Mean Squared Error: 0.16058479249477386\n",
"\u001b[1m146/146\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n"
]
}
],
"source": [
"# Neural network model\n",
"model = Sequential([\n",
" Dense(64, activation='relu', input_shape=(feature_train_scaled.shape[1],)),\n",
" Dense(64, activation='relu'),\n",
" Dense(1)\n",
"])\n",
"\n",
"# Compile and train the model\n",
"model.compile(optimizer='adam', loss='mean_squared_error')\n",
"model.fit(feature_train_scaled, target_train, epochs=50, batch_size=32, verbose=1)\n",
"\n",
"# Evaluate the model\n",
"mse = model.evaluate(feature_test_scaled, target_test)\n",
"print(\"Mean Squared Error:\", mse)\n",
"\n",
"# Predictions and performance metrics\n",
"target_prediction = model.predict(feature_test_scaled)\n",
"r2 = r2_score(target_test, target_prediction)\n",
"mae = mean_absolute_error(target_test, target_prediction)\n",
"mse = mean_squared_error(target_test, target_prediction)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R-squared: 0.9780861666108605\n",
"Mean Absolute Error: 0.7006260730692777\n",
"Mean Squared Error: 2.554603752569432\n",
"p-value: 0.0000\n",
"Root Squared Error: 1.60\n",
"F-statistic: 24052.88\n"
]
}
],
"source": [
"# Calculate F-statistic and p-value \n",
"n_samples = len(target)\n",
"n_predictors = feature_train_scaled.shape[1]\n",
"residual = n_samples - n_predictors - 1\n",
"explained_variance = r2 * np.sum((target - np.mean(target))**2)\n",
"unexplained_variance = mse * n_samples\n",
"\n",
"F_value = (explained_variance / n_predictors) / (unexplained_variance / residual)\n",
"p_value = 1 - f.cdf(F_value, n_predictors, residual)\n",
"rse = np.sqrt(mse)\n",
"\n",
"# Print the results\n",
"print(f\"R-squared: {r2}\")\n",
"print(f\"Mean Absolute Error: {mae}\")\n",
"print(f\"Mean Squared Error: {mse}\")\n",
"print(f\"p-value: {p_value:.4f}\")\n",
"print(f\"Root Squared Error: {rse:.2f}\")\n",
"print(f\"F-statistic: {F_value:.2f}\")"
]
}
],
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|