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| { | |
| "cells": [ | |
| { | |
| "metadata": {}, | |
| "id": "23279796", | |
| "cell_type": "markdown", | |
| "source": "# ANN (GAS TURBINES)" | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2023-07-19T04:05:06.333662Z", | |
| "start_time": "2023-07-19T04:05:05.382596Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "id": "038141db", | |
| "cell_type": "code", | |
| "source": "import pandas as pd\nimport numpy as np", | |
| "execution_count": 1, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2023-07-19T04:05:06.381424Z", | |
| "start_time": "2023-07-19T04:05:06.333891Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "id": "ce42e743", | |
| "cell_type": "code", | |
| "source": "# read the data set and basic eda\ndf=pd.read_csv('gas_turbines.csv')", | |
| "execution_count": 2, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2023-07-19T04:05:06.445985Z", | |
| "start_time": "2023-07-19T04:05:06.384901Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "id": "150cf077", | |
| "cell_type": "code", | |
| "source": "df.describe()", | |
| "execution_count": 3, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>AT</th>\n <th>AP</th>\n <th>AH</th>\n <th>AFDP</th>\n <th>GTEP</th>\n <th>TIT</th>\n <th>TAT</th>\n <th>TEY</th>\n <th>CDP</th>\n <th>CO</th>\n <th>NOX</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td>15039.000000</td>\n <td>15039.00000</td>\n <td>15039.000000</td>\n <td>15039.000000</td>\n <td>15039.000000</td>\n <td>15039.000000</td>\n <td>15039.000000</td>\n <td>15039.000000</td>\n <td>15039.000000</td>\n <td>15039.000000</td>\n <td>15039.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>17.764381</td>\n <td>1013.19924</td>\n <td>79.124174</td>\n <td>4.200294</td>\n <td>25.419061</td>\n <td>1083.798770</td>\n <td>545.396183</td>\n <td>134.188464</td>\n <td>12.102353</td>\n <td>1.972499</td>\n <td>68.190934</td>\n </tr>\n <tr>\n <th>std</th>\n <td>7.574323</td>\n <td>6.41076</td>\n <td>13.793439</td>\n <td>0.760197</td>\n <td>4.173916</td>\n <td>16.527806</td>\n <td>7.866803</td>\n <td>15.829717</td>\n <td>1.103196</td>\n <td>2.222206</td>\n <td>10.470586</td>\n </tr>\n <tr>\n <th>min</th>\n <td>0.522300</td>\n <td>985.85000</td>\n <td>30.344000</td>\n <td>2.087400</td>\n <td>17.878000</td>\n <td>1000.800000</td>\n <td>512.450000</td>\n <td>100.170000</td>\n <td>9.904400</td>\n <td>0.000388</td>\n <td>27.765000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>11.408000</td>\n <td>1008.90000</td>\n <td>69.750000</td>\n <td>3.723900</td>\n <td>23.294000</td>\n <td>1079.600000</td>\n <td>542.170000</td>\n <td>127.985000</td>\n <td>11.622000</td>\n <td>0.858055</td>\n <td>61.303500</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>18.186000</td>\n <td>1012.80000</td>\n <td>82.266000</td>\n <td>4.186200</td>\n <td>25.082000</td>\n <td>1088.700000</td>\n <td>549.890000</td>\n <td>133.780000</td>\n <td>12.025000</td>\n <td>1.390200</td>\n <td>66.601000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>23.862500</td>\n <td>1016.90000</td>\n <td>90.043500</td>\n <td>4.550900</td>\n <td>27.184000</td>\n <td>1096.000000</td>\n <td>550.060000</td>\n <td>140.895000</td>\n <td>12.578000</td>\n <td>2.160400</td>\n <td>73.935500</td>\n </tr>\n <tr>\n <th>max</th>\n <td>34.929000</td>\n <td>1034.20000</td>\n <td>100.200000</td>\n <td>7.610600</td>\n <td>37.402000</td>\n <td>1100.800000</td>\n <td>550.610000</td>\n <td>174.610000</td>\n <td>15.081000</td>\n <td>44.103000</td>\n <td>119.890000</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " AT AP AH AFDP GTEP \\\ncount 15039.000000 15039.00000 15039.000000 15039.000000 15039.000000 \nmean 17.764381 1013.19924 79.124174 4.200294 25.419061 \nstd 7.574323 6.41076 13.793439 0.760197 4.173916 \nmin 0.522300 985.85000 30.344000 2.087400 17.878000 \n25% 11.408000 1008.90000 69.750000 3.723900 23.294000 \n50% 18.186000 1012.80000 82.266000 4.186200 25.082000 \n75% 23.862500 1016.90000 90.043500 4.550900 27.184000 \nmax 34.929000 1034.20000 100.200000 7.610600 37.402000 \n\n TIT TAT TEY CDP CO \\\ncount 15039.000000 15039.000000 15039.000000 15039.000000 15039.000000 \nmean 1083.798770 545.396183 134.188464 12.102353 1.972499 \nstd 16.527806 7.866803 15.829717 1.103196 2.222206 \nmin 1000.800000 512.450000 100.170000 9.904400 0.000388 \n25% 1079.600000 542.170000 127.985000 11.622000 0.858055 \n50% 1088.700000 549.890000 133.780000 12.025000 1.390200 \n75% 1096.000000 550.060000 140.895000 12.578000 2.160400 \nmax 1100.800000 550.610000 174.610000 15.081000 44.103000 \n\n NOX \ncount 15039.000000 \nmean 68.190934 \nstd 10.470586 \nmin 27.765000 \n25% 61.303500 \n50% 66.601000 \n75% 73.935500 \nmax 119.890000 " | |
| }, | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2023-07-19T04:05:06.462334Z", | |
| "start_time": "2023-07-19T04:05:06.448191Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "id": "535bbb25", | |
| "cell_type": "code", | |
| "source": "# create the dammies\ntey=pd.cut(df['TEY'],bins=[-100.17,133.78,174.61],labels=['low energy yeild','high energy yeild'])\ndf['TEY']=tey", | |
| "execution_count": 4, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2023-07-19T04:05:07.393480Z", | |
| "start_time": "2023-07-19T04:05:06.462334Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "id": "1528562c", | |
| "cell_type": "code", | |
| "source": "# lable the data set\nfrom sklearn.preprocessing import LabelEncoder\nle=LabelEncoder()\ndf['TEY']=1-le.fit_transform(df['TEY'])\ndf", | |
| "execution_count": 5, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>AT</th>\n <th>AP</th>\n <th>AH</th>\n <th>AFDP</th>\n <th>GTEP</th>\n <th>TIT</th>\n <th>TAT</th>\n <th>TEY</th>\n <th>CDP</th>\n <th>CO</th>\n <th>NOX</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>6.8594</td>\n <td>1007.9</td>\n <td>96.799</td>\n <td>3.5000</td>\n <td>19.663</td>\n <td>1059.2</td>\n <td>550.00</td>\n <td>0</td>\n <td>10.605</td>\n <td>3.1547</td>\n <td>82.722</td>\n </tr>\n <tr>\n <th>1</th>\n <td>6.7850</td>\n <td>1008.4</td>\n <td>97.118</td>\n <td>3.4998</td>\n <td>19.728</td>\n <td>1059.3</td>\n <td>550.00</td>\n <td>0</td>\n <td>10.598</td>\n <td>3.2363</td>\n <td>82.776</td>\n </tr>\n <tr>\n <th>2</th>\n <td>6.8977</td>\n <td>1008.8</td>\n <td>95.939</td>\n <td>3.4824</td>\n <td>19.779</td>\n <td>1059.4</td>\n <td>549.87</td>\n <td>0</td>\n <td>10.601</td>\n <td>3.2012</td>\n <td>82.468</td>\n </tr>\n <tr>\n <th>3</th>\n <td>7.0569</td>\n <td>1009.2</td>\n <td>95.249</td>\n <td>3.4805</td>\n <td>19.792</td>\n <td>1059.6</td>\n <td>549.99</td>\n <td>0</td>\n <td>10.606</td>\n <td>3.1923</td>\n <td>82.670</td>\n </tr>\n <tr>\n <th>4</th>\n <td>7.3978</td>\n <td>1009.7</td>\n <td>95.150</td>\n <td>3.4976</td>\n <td>19.765</td>\n <td>1059.7</td>\n <td>549.98</td>\n <td>0</td>\n <td>10.612</td>\n <td>3.2484</td>\n <td>82.311</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>15034</th>\n <td>9.0301</td>\n <td>1005.6</td>\n <td>98.460</td>\n <td>3.5421</td>\n <td>19.164</td>\n <td>1049.7</td>\n <td>546.21</td>\n <td>0</td>\n <td>10.400</td>\n <td>4.5186</td>\n <td>79.559</td>\n </tr>\n <tr>\n <th>15035</th>\n <td>7.8879</td>\n <td>1005.9</td>\n <td>99.093</td>\n <td>3.5059</td>\n <td>19.414</td>\n <td>1046.3</td>\n <td>543.22</td>\n <td>0</td>\n <td>10.433</td>\n <td>4.8470</td>\n <td>79.917</td>\n </tr>\n <tr>\n <th>15036</th>\n <td>7.2647</td>\n <td>1006.3</td>\n <td>99.496</td>\n <td>3.4770</td>\n <td>19.530</td>\n <td>1037.7</td>\n <td>537.32</td>\n <td>0</td>\n <td>10.483</td>\n <td>7.9632</td>\n <td>90.912</td>\n </tr>\n <tr>\n <th>15037</th>\n <td>7.0060</td>\n <td>1006.8</td>\n <td>99.008</td>\n <td>3.4486</td>\n <td>19.377</td>\n <td>1043.2</td>\n <td>541.24</td>\n <td>0</td>\n <td>10.533</td>\n <td>6.2494</td>\n <td>93.227</td>\n </tr>\n <tr>\n <th>15038</th>\n <td>6.9279</td>\n <td>1007.2</td>\n <td>97.533</td>\n <td>3.4275</td>\n <td>19.306</td>\n <td>1049.9</td>\n <td>545.85</td>\n <td>0</td>\n <td>10.583</td>\n <td>4.9816</td>\n <td>92.498</td>\n </tr>\n </tbody>\n</table>\n<p>15039 rows × 11 columns</p>\n</div>", | |
| "text/plain": " AT AP AH AFDP GTEP TIT TAT TEY CDP \\\n0 6.8594 1007.9 96.799 3.5000 19.663 1059.2 550.00 0 10.605 \n1 6.7850 1008.4 97.118 3.4998 19.728 1059.3 550.00 0 10.598 \n2 6.8977 1008.8 95.939 3.4824 19.779 1059.4 549.87 0 10.601 \n3 7.0569 1009.2 95.249 3.4805 19.792 1059.6 549.99 0 10.606 \n4 7.3978 1009.7 95.150 3.4976 19.765 1059.7 549.98 0 10.612 \n... ... ... ... ... ... ... ... ... ... \n15034 9.0301 1005.6 98.460 3.5421 19.164 1049.7 546.21 0 10.400 \n15035 7.8879 1005.9 99.093 3.5059 19.414 1046.3 543.22 0 10.433 \n15036 7.2647 1006.3 99.496 3.4770 19.530 1037.7 537.32 0 10.483 \n15037 7.0060 1006.8 99.008 3.4486 19.377 1043.2 541.24 0 10.533 \n15038 6.9279 1007.2 97.533 3.4275 19.306 1049.9 545.85 0 10.583 \n\n CO NOX \n0 3.1547 82.722 \n1 3.2363 82.776 \n2 3.2012 82.468 \n3 3.1923 82.670 \n4 3.2484 82.311 \n... ... ... \n15034 4.5186 79.559 \n15035 4.8470 79.917 \n15036 7.9632 90.912 \n15037 6.2494 93.227 \n15038 4.9816 92.498 \n\n[15039 rows x 11 columns]" | |
| }, | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2023-07-19T04:05:07.409066Z", | |
| "start_time": "2023-07-19T04:05:07.396061Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "id": "b5431985", | |
| "cell_type": "code", | |
| "source": "X=df.drop(['TEY'],axis=1)\nY=df['TEY']", | |
| "execution_count": 6, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2023-07-19T04:05:07.498329Z", | |
| "start_time": "2023-07-19T04:05:07.412598Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "id": "fec9c266", | |
| "cell_type": "code", | |
| "source": "# test and train the data set\nfrom sklearn.model_selection import train_test_split\nX_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,random_state=42)", | |
| "execution_count": 7, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2023-07-19T04:07:31.652659Z", | |
| "start_time": "2023-07-19T04:05:07.498329Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "id": "94b2312b", | |
| "cell_type": "code", | |
| "source": "# Initialising ANN\nimport tensorflow as tf\nann = tf.keras.models.Sequential()\n#Adding First Hidden Layer\nann.add(tf.keras.layers.Dense(units=10,activation=\"relu\"))\n#Adding Second Hidden Layer\nann.add(tf.keras.layers.Dense(units=13,activation=\"relu\"))\n#Adding Output Layer\nann.add(tf.keras.layers.Dense(units=1,activation=\"sigmoid\"))\n#Compiling ANN\nann.compile(optimizer=\"adam\",loss=\"binary_crossentropy\",metrics=['accuracy'])\n#Fitting ANN\nann.fit(X_train,Y_train,batch_size=32,epochs = 100)", | |
| "execution_count": 8, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": "Epoch 1/100\n376/376 [==============================] - 2s 4ms/step - loss: 1.4782 - accuracy: 0.7166\nEpoch 2/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.4242 - accuracy: 0.7640\nEpoch 3/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3985 - accuracy: 0.7731\nEpoch 4/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.4019 - accuracy: 0.7700\nEpoch 5/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3847 - accuracy: 0.7765\nEpoch 6/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3751 - accuracy: 0.7825\nEpoch 7/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3790 - accuracy: 0.7811\nEpoch 8/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3749 - accuracy: 0.7838\nEpoch 9/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3894 - accuracy: 0.7819\nEpoch 10/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3531 - accuracy: 0.7950\nEpoch 11/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3583 - accuracy: 0.7877\nEpoch 12/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3694 - accuracy: 0.7885\nEpoch 13/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3623 - accuracy: 0.7902\nEpoch 14/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3486 - accuracy: 0.7954\nEpoch 15/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3512 - accuracy: 0.7975\nEpoch 16/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3492 - accuracy: 0.7954\nEpoch 17/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3418 - accuracy: 0.8027\nEpoch 18/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3611 - accuracy: 0.7894\nEpoch 19/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3476 - accuracy: 0.7995\nEpoch 20/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3463 - accuracy: 0.7961\nEpoch 21/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3507 - accuracy: 0.7880\nEpoch 22/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3397 - accuracy: 0.8016\nEpoch 23/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3401 - accuracy: 0.8008\nEpoch 24/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3332 - accuracy: 0.8038\nEpoch 25/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3433 - accuracy: 0.7977\nEpoch 26/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3371 - accuracy: 0.8058\nEpoch 27/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3436 - accuracy: 0.7951\nEpoch 28/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3404 - accuracy: 0.7998\nEpoch 29/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3386 - accuracy: 0.7978\nEpoch 30/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3371 - accuracy: 0.8024\nEpoch 31/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3348 - accuracy: 0.8041\nEpoch 32/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3273 - accuracy: 0.8107\nEpoch 33/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3251 - accuracy: 0.8128\nEpoch 34/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3443 - accuracy: 0.8015\nEpoch 35/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3364 - accuracy: 0.7978\nEpoch 36/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3374 - accuracy: 0.8042\nEpoch 37/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3256 - accuracy: 0.8066\nEpoch 38/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3269 - accuracy: 0.8075\nEpoch 39/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3518 - accuracy: 0.7967\nEpoch 40/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3191 - accuracy: 0.8187\nEpoch 41/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3227 - accuracy: 0.8086\nEpoch 42/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3245 - accuracy: 0.8067\nEpoch 43/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3280 - accuracy: 0.8082\nEpoch 44/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3151 - accuracy: 0.8185\nEpoch 45/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3280 - accuracy: 0.8121\nEpoch 46/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3189 - accuracy: 0.8171\nEpoch 47/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3242 - accuracy: 0.8092\nEpoch 48/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3220 - accuracy: 0.8106\nEpoch 49/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3136 - accuracy: 0.8177\nEpoch 50/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3184 - accuracy: 0.8186\nEpoch 51/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3138 - accuracy: 0.8239\nEpoch 52/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3171 - accuracy: 0.8153\nEpoch 53/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3260 - accuracy: 0.8128\nEpoch 54/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3139 - accuracy: 0.8195\nEpoch 55/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3236 - accuracy: 0.8051\nEpoch 56/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3154 - accuracy: 0.8166\nEpoch 57/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3136 - accuracy: 0.8167\nEpoch 58/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3040 - accuracy: 0.8309\nEpoch 59/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3087 - accuracy: 0.8240\nEpoch 60/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3208 - accuracy: 0.8157\nEpoch 61/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3133 - accuracy: 0.8219\nEpoch 62/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3150 - accuracy: 0.8177\nEpoch 63/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3156 - accuracy: 0.8171\nEpoch 64/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3156 - accuracy: 0.8133\nEpoch 65/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3134 - accuracy: 0.8176\nEpoch 66/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3168 - accuracy: 0.8190\nEpoch 67/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3068 - accuracy: 0.8269\nEpoch 68/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3104 - accuracy: 0.8244\nEpoch 69/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3097 - accuracy: 0.8216\nEpoch 70/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3220 - accuracy: 0.8050\nEpoch 71/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3130 - accuracy: 0.8195\nEpoch 72/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3094 - accuracy: 0.8245\nEpoch 73/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3140 - accuracy: 0.8171\nEpoch 74/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3177 - accuracy: 0.8172\nEpoch 75/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3108 - accuracy: 0.8200\nEpoch 76/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3090 - accuracy: 0.8216\nEpoch 77/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3107 - accuracy: 0.8205\nEpoch 78/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3077 - accuracy: 0.8222\nEpoch 79/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3041 - accuracy: 0.8270\nEpoch 80/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3113 - accuracy: 0.8212\nEpoch 81/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3097 - accuracy: 0.8224\nEpoch 82/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3064 - accuracy: 0.8225\nEpoch 83/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3123 - accuracy: 0.8174\nEpoch 84/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3103 - accuracy: 0.8235\nEpoch 85/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3083 - accuracy: 0.8238\nEpoch 86/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3018 - accuracy: 0.8266\nEpoch 87/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3103 - accuracy: 0.8231\nEpoch 88/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3157 - accuracy: 0.8107\nEpoch 89/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3107 - accuracy: 0.8186\nEpoch 90/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3115 - accuracy: 0.8244\nEpoch 91/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3066 - accuracy: 0.8230\nEpoch 92/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3089 - accuracy: 0.8212\nEpoch 93/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3082 - accuracy: 0.8254\nEpoch 94/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3026 - accuracy: 0.8264\nEpoch 95/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3147 - accuracy: 0.8183\nEpoch 96/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3097 - accuracy: 0.8221\nEpoch 97/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3055 - accuracy: 0.8240\nEpoch 98/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3072 - accuracy: 0.8196\nEpoch 99/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3029 - accuracy: 0.8303\nEpoch 100/100\n376/376 [==============================] - 1s 4ms/step - loss: 0.3145 - accuracy: 0.8202\n" | |
| }, | |
| { | |
| "data": { | |
| "text/plain": "<keras.callbacks.History at 0x229b2178f40>" | |
| }, | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2023-07-19T04:07:32.356571Z", | |
| "start_time": "2023-07-19T04:07:31.652659Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "id": "f2af0f9f", | |
| "cell_type": "code", | |
| "source": "# classification report\nthreshold = 0.50\nY_preds = np.where(ann.predict(X_test)> threshold, 1, 0)\nY_preds", | |
| "execution_count": 9, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": "94/94 [==============================] - 0s 3ms/step\n" | |
| }, | |
| { | |
| "data": { | |
| "text/plain": "array([[1],\n [0],\n [0],\n ...,\n [1],\n [1],\n [0]])" | |
| }, | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2023-07-19T04:07:32.372365Z", | |
| "start_time": "2023-07-19T04:07:32.356571Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "id": "58512367", | |
| "cell_type": "code", | |
| "source": "Y_preds = Y_preds.ravel()\nY_preds", | |
| "execution_count": 10, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": "array([1, 0, 0, ..., 1, 1, 0])" | |
| }, | |
| "execution_count": 10, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2023-07-19T04:07:32.428673Z", | |
| "start_time": "2023-07-19T04:07:32.378126Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "id": "2ceaaf53", | |
| "cell_type": "code", | |
| "source": "Y", | |
| "execution_count": 11, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": "0 0\n1 0\n2 0\n3 0\n4 0\n ..\n15034 0\n15035 0\n15036 0\n15037 0\n15038 0\nName: TEY, Length: 15039, dtype: int32" | |
| }, | |
| "execution_count": 11, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2023-07-19T04:07:32.461460Z", | |
| "start_time": "2023-07-19T04:07:32.429670Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "id": "80f410f7", | |
| "cell_type": "code", | |
| "source": "np.mean(Y_preds==Y_test)", | |
| "execution_count": 12, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": "0.8427526595744681" | |
| }, | |
| "execution_count": 12, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "id": "5c0d81b8", | |
| "cell_type": "markdown", | |
| "source": "# ANN (FOREST FIRES)" | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2023-07-19T04:07:32.478085Z", | |
| "start_time": "2023-07-19T04:07:32.468120Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "id": "2787824d", | |
| "cell_type": "code", | |
| "source": "import pandas as pd\nimport numpy as np", | |
| "execution_count": 13, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2023-07-19T04:07:32.518964Z", | |
| "start_time": "2023-07-19T04:07:32.486255Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "id": "a5fc190a", | |
| "cell_type": "code", | |
| "source": "dt=pd.read_csv('forestfires (1).csv')", | |
| "execution_count": 14, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2023-07-19T04:07:32.567787Z", | |
| "start_time": "2023-07-19T04:07:32.518964Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "id": "30bd434f", | |
| "cell_type": "code", | |
| "source": "dt.info()", | |
| "execution_count": 15, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 517 entries, 0 to 516\nData columns (total 31 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 month 517 non-null object \n 1 day 517 non-null object \n 2 FFMC 517 non-null float64\n 3 DMC 517 non-null float64\n 4 DC 517 non-null float64\n 5 ISI 517 non-null float64\n 6 temp 517 non-null float64\n 7 RH 517 non-null int64 \n 8 wind 517 non-null float64\n 9 rain 517 non-null float64\n 10 area 517 non-null float64\n 11 dayfri 517 non-null int64 \n 12 daymon 517 non-null int64 \n 13 daysat 517 non-null int64 \n 14 daysun 517 non-null int64 \n 15 daythu 517 non-null int64 \n 16 daytue 517 non-null int64 \n 17 daywed 517 non-null int64 \n 18 monthapr 517 non-null int64 \n 19 monthaug 517 non-null int64 \n 20 monthdec 517 non-null int64 \n 21 monthfeb 517 non-null int64 \n 22 monthjan 517 non-null int64 \n 23 monthjul 517 non-null int64 \n 24 monthjun 517 non-null int64 \n 25 monthmar 517 non-null int64 \n 26 monthmay 517 non-null int64 \n 27 monthnov 517 non-null int64 \n 28 monthoct 517 non-null int64 \n 29 monthsep 517 non-null int64 \n 30 size_category 517 non-null object \ndtypes: float64(8), int64(20), object(3)\nmemory usage: 125.3+ KB\n" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2023-07-19T04:07:32.736429Z", | |
| "start_time": "2023-07-19T04:07:32.570358Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "id": "fbcd6dd0", | |
| "cell_type": "code", | |
| "source": "dt.describe()", | |
| "execution_count": 16, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>FFMC</th>\n <th>DMC</th>\n <th>DC</th>\n <th>ISI</th>\n <th>temp</th>\n <th>RH</th>\n <th>wind</th>\n <th>rain</th>\n <th>area</th>\n <th>dayfri</th>\n <th>...</th>\n <th>monthdec</th>\n <th>monthfeb</th>\n <th>monthjan</th>\n <th>monthjul</th>\n <th>monthjun</th>\n <th>monthmar</th>\n <th>monthmay</th>\n <th>monthnov</th>\n <th>monthoct</th>\n <th>monthsep</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td>517.000000</td>\n <td>517.000000</td>\n <td>517.000000</td>\n <td>517.000000</td>\n <td>517.000000</td>\n <td>517.000000</td>\n <td>517.000000</td>\n <td>517.000000</td>\n <td>517.000000</td>\n <td>517.000000</td>\n <td>...</td>\n <td>517.000000</td>\n <td>517.000000</td>\n <td>517.000000</td>\n <td>517.000000</td>\n <td>517.000000</td>\n <td>517.000000</td>\n <td>517.000000</td>\n <td>517.000000</td>\n <td>517.000000</td>\n <td>517.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>90.644681</td>\n <td>110.872340</td>\n <td>547.940039</td>\n <td>9.021663</td>\n <td>18.889168</td>\n <td>44.288201</td>\n <td>4.017602</td>\n <td>0.021663</td>\n <td>12.847292</td>\n <td>0.164410</td>\n <td>...</td>\n <td>0.017408</td>\n <td>0.038685</td>\n <td>0.003868</td>\n <td>0.061896</td>\n <td>0.032882</td>\n <td>0.104449</td>\n <td>0.003868</td>\n <td>0.001934</td>\n <td>0.029014</td>\n <td>0.332689</td>\n </tr>\n <tr>\n <th>std</th>\n <td>5.520111</td>\n <td>64.046482</td>\n <td>248.066192</td>\n <td>4.559477</td>\n <td>5.806625</td>\n <td>16.317469</td>\n <td>1.791653</td>\n <td>0.295959</td>\n <td>63.655818</td>\n <td>0.371006</td>\n <td>...</td>\n <td>0.130913</td>\n <td>0.193029</td>\n <td>0.062137</td>\n <td>0.241199</td>\n <td>0.178500</td>\n <td>0.306138</td>\n <td>0.062137</td>\n <td>0.043980</td>\n <td>0.168007</td>\n <td>0.471632</td>\n </tr>\n <tr>\n <th>min</th>\n <td>18.700000</td>\n <td>1.100000</td>\n <td>7.900000</td>\n <td>0.000000</td>\n <td>2.200000</td>\n <td>15.000000</td>\n <td>0.400000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>...</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>90.200000</td>\n <td>68.600000</td>\n <td>437.700000</td>\n <td>6.500000</td>\n <td>15.500000</td>\n <td>33.000000</td>\n <td>2.700000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>...</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>91.600000</td>\n <td>108.300000</td>\n <td>664.200000</td>\n <td>8.400000</td>\n <td>19.300000</td>\n <td>42.000000</td>\n <td>4.000000</td>\n <td>0.000000</td>\n <td>0.520000</td>\n <td>0.000000</td>\n <td>...</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>92.900000</td>\n <td>142.400000</td>\n <td>713.900000</td>\n <td>10.800000</td>\n <td>22.800000</td>\n <td>53.000000</td>\n <td>4.900000</td>\n <td>0.000000</td>\n <td>6.570000</td>\n <td>0.000000</td>\n <td>...</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>1.000000</td>\n </tr>\n <tr>\n <th>max</th>\n <td>96.200000</td>\n <td>291.300000</td>\n <td>860.600000</td>\n <td>56.100000</td>\n <td>33.300000</td>\n <td>100.000000</td>\n <td>9.400000</td>\n <td>6.400000</td>\n <td>1090.840000</td>\n <td>1.000000</td>\n <td>...</td>\n <td>1.000000</td>\n <td>1.000000</td>\n <td>1.000000</td>\n <td>1.000000</td>\n <td>1.000000</td>\n <td>1.000000</td>\n <td>1.000000</td>\n <td>1.000000</td>\n <td>1.000000</td>\n <td>1.000000</td>\n </tr>\n </tbody>\n</table>\n<p>8 rows × 28 columns</p>\n</div>", | |
| "text/plain": " FFMC DMC DC ISI temp RH \\\ncount 517.000000 517.000000 517.000000 517.000000 517.000000 517.000000 \nmean 90.644681 110.872340 547.940039 9.021663 18.889168 44.288201 \nstd 5.520111 64.046482 248.066192 4.559477 5.806625 16.317469 \nmin 18.700000 1.100000 7.900000 0.000000 2.200000 15.000000 \n25% 90.200000 68.600000 437.700000 6.500000 15.500000 33.000000 \n50% 91.600000 108.300000 664.200000 8.400000 19.300000 42.000000 \n75% 92.900000 142.400000 713.900000 10.800000 22.800000 53.000000 \nmax 96.200000 291.300000 860.600000 56.100000 33.300000 100.000000 \n\n wind rain area dayfri ... monthdec \\\ncount 517.000000 517.000000 517.000000 517.000000 ... 517.000000 \nmean 4.017602 0.021663 12.847292 0.164410 ... 0.017408 \nstd 1.791653 0.295959 63.655818 0.371006 ... 0.130913 \nmin 0.400000 0.000000 0.000000 0.000000 ... 0.000000 \n25% 2.700000 0.000000 0.000000 0.000000 ... 0.000000 \n50% 4.000000 0.000000 0.520000 0.000000 ... 0.000000 \n75% 4.900000 0.000000 6.570000 0.000000 ... 0.000000 \nmax 9.400000 6.400000 1090.840000 1.000000 ... 1.000000 \n\n monthfeb monthjan monthjul monthjun monthmar monthmay \\\ncount 517.000000 517.000000 517.000000 517.000000 517.000000 517.000000 \nmean 0.038685 0.003868 0.061896 0.032882 0.104449 0.003868 \nstd 0.193029 0.062137 0.241199 0.178500 0.306138 0.062137 \nmin 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n25% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n50% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n75% 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \nmax 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 \n\n monthnov monthoct monthsep \ncount 517.000000 517.000000 517.000000 \nmean 0.001934 0.029014 0.332689 \nstd 0.043980 0.168007 0.471632 \nmin 0.000000 0.000000 0.000000 \n25% 0.000000 0.000000 0.000000 \n50% 0.000000 0.000000 0.000000 \n75% 0.000000 0.000000 1.000000 \nmax 1.000000 1.000000 1.000000 \n\n[8 rows x 28 columns]" | |
| }, | |
| "execution_count": 16, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2023-07-19T04:07:32.768491Z", | |
| "start_time": "2023-07-19T04:07:32.736429Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "id": "08b5b68e", | |
| "cell_type": "code", | |
| "source": "forest=dt.iloc[:,2:]", | |
| "execution_count": 17, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2023-07-19T04:07:32.833299Z", | |
| "start_time": "2023-07-19T04:07:32.768791Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "id": "f440c03b", | |
| "cell_type": "code", | |
| "source": "# label encoding the data set\nfrom sklearn.preprocessing import LabelEncoder\nle=LabelEncoder()\nforest['size_category']=1-le.fit_transform(forest['size_category'])\nforest.tail()", | |
| "execution_count": 18, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>FFMC</th>\n <th>DMC</th>\n <th>DC</th>\n <th>ISI</th>\n <th>temp</th>\n <th>RH</th>\n <th>wind</th>\n <th>rain</th>\n <th>area</th>\n <th>dayfri</th>\n <th>...</th>\n <th>monthfeb</th>\n <th>monthjan</th>\n <th>monthjul</th>\n <th>monthjun</th>\n <th>monthmar</th>\n <th>monthmay</th>\n <th>monthnov</th>\n <th>monthoct</th>\n <th>monthsep</th>\n <th>size_category</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>512</th>\n <td>81.6</td>\n <td>56.7</td>\n <td>665.6</td>\n <td>1.9</td>\n <td>27.8</td>\n <td>32</td>\n <td>2.7</td>\n <td>0.0</td>\n <td>6.44</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>513</th>\n <td>81.6</td>\n <td>56.7</td>\n <td>665.6</td>\n <td>1.9</td>\n <td>21.9</td>\n <td>71</td>\n <td>5.8</td>\n <td>0.0</td>\n <td>54.29</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>514</th>\n <td>81.6</td>\n <td>56.7</td>\n <td>665.6</td>\n <td>1.9</td>\n <td>21.2</td>\n <td>70</td>\n <td>6.7</td>\n <td>0.0</td>\n <td>11.16</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>515</th>\n <td>94.4</td>\n <td>146.0</td>\n <td>614.7</td>\n <td>11.3</td>\n <td>25.6</td>\n <td>42</td>\n <td>4.0</td>\n <td>0.0</td>\n <td>0.00</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>516</th>\n <td>79.5</td>\n <td>3.0</td>\n <td>106.7</td>\n <td>1.1</td>\n <td>11.8</td>\n <td>31</td>\n <td>4.5</td>\n <td>0.0</td>\n <td>0.00</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 29 columns</p>\n</div>", | |
| "text/plain": " FFMC DMC DC ISI temp RH wind rain area dayfri ... \\\n512 81.6 56.7 665.6 1.9 27.8 32 2.7 0.0 6.44 0 ... \n513 81.6 56.7 665.6 1.9 21.9 71 5.8 0.0 54.29 0 ... \n514 81.6 56.7 665.6 1.9 21.2 70 6.7 0.0 11.16 0 ... \n515 94.4 146.0 614.7 11.3 25.6 42 4.0 0.0 0.00 0 ... \n516 79.5 3.0 106.7 1.1 11.8 31 4.5 0.0 0.00 0 ... \n\n monthfeb monthjan monthjul monthjun monthmar monthmay monthnov \\\n512 0 0 0 0 0 0 0 \n513 0 0 0 0 0 0 0 \n514 0 0 0 0 0 0 0 \n515 0 0 0 0 0 0 0 \n516 0 0 0 0 0 0 1 \n\n monthoct monthsep size_category \n512 0 0 1 \n513 0 0 1 \n514 0 0 1 \n515 0 0 0 \n516 0 0 0 \n\n[5 rows x 29 columns]" | |
| }, | |
| "execution_count": 18, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2023-07-19T04:07:32.857865Z", | |
| "start_time": "2023-07-19T04:07:32.833299Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "id": "47a80171", | |
| "cell_type": "code", | |
| "source": "#split the data set\nX1=forest.drop(['size_category'],axis=1)\nY1=forest['size_category']", | |
| "execution_count": 19, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2023-07-19T04:07:32.874435Z", | |
| "start_time": "2023-07-19T04:07:32.859647Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "id": "b7290953", | |
| "cell_type": "code", | |
| "source": "# test and train the data set\nfrom sklearn.model_selection import train_test_split\nx_train,x_test,y_train,y_test=train_test_split(X1,Y1,test_size=0.2,random_state=42)", | |
| "execution_count": 20, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2023-07-19T04:07:32.907443Z", | |
| "start_time": "2023-07-19T04:07:32.875525Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "id": "3854a400", | |
| "cell_type": "code", | |
| "source": "#Performing Feature Scaling\nfrom sklearn.preprocessing import StandardScaler\nsc = StandardScaler()\nx_train = sc.fit_transform(x_train)\nx_test = sc.transform(x_test)", | |
| "execution_count": 21, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2023-07-19T04:07:32.923368Z", | |
| "start_time": "2023-07-19T04:07:32.907443Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "id": "3d34cfe0", | |
| "cell_type": "code", | |
| "source": "from keras.models import Sequential\nfrom keras.layers import Dense", | |
| "execution_count": 22, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2023-07-19T04:07:39.949993Z", | |
| "start_time": "2023-07-19T04:07:32.924236Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "id": "8e79e6a8", | |
| "cell_type": "code", | |
| "source": "# Initialising ANN\nimport tensorflow as tf\nmodel= tf.keras.models.Sequential()\n#Adding First Hidden Layer\nmodel.add(tf.keras.layers.Dense(units=6,activation=\"relu\"))\n#Adding Second Hidden Layer\nmodel.add(tf.keras.layers.Dense(units=6,activation=\"relu\"))\n#Adding Output Layer\nmodel.add(tf.keras.layers.Dense(units=1,activation=\"sigmoid\"))\n#Compiling ANN\nmodel.compile(optimizer=\"adam\",loss=\"binary_crossentropy\",metrics=['accuracy'])\n#Fitting ANN\nmodel.fit(x_train,y_train,batch_size=32,epochs = 100)", | |
| "execution_count": 23, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": "Epoch 1/100\n13/13 [==============================] - 1s 3ms/step - loss: 0.7189 - accuracy: 0.5157\nEpoch 2/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.7031 - accuracy: 0.5545\nEpoch 3/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.6895 - accuracy: 0.5908\nEpoch 4/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.6759 - accuracy: 0.6368\nEpoch 5/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.6639 - accuracy: 0.6610\nEpoch 6/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.6523 - accuracy: 0.7022\nEpoch 7/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.6411 - accuracy: 0.7119\nEpoch 8/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.6287 - accuracy: 0.7312\nEpoch 9/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.6178 - accuracy: 0.7337\nEpoch 10/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.6080 - accuracy: 0.7337\nEpoch 11/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.5992 - accuracy: 0.7385\nEpoch 12/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.5913 - accuracy: 0.7385\nEpoch 13/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.5840 - accuracy: 0.7385\nEpoch 14/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.5780 - accuracy: 0.7385\nEpoch 15/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.5723 - accuracy: 0.7385\nEpoch 16/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.5666 - accuracy: 0.7385\nEpoch 17/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.5618 - accuracy: 0.7385\nEpoch 18/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.5574 - accuracy: 0.7385\nEpoch 19/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.5532 - accuracy: 0.7385\nEpoch 20/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.5493 - accuracy: 0.7385\nEpoch 21/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.5453 - accuracy: 0.7385\nEpoch 22/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.5413 - accuracy: 0.7385\nEpoch 23/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.5369 - accuracy: 0.7409\nEpoch 24/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.5328 - accuracy: 0.7409\nEpoch 25/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.5286 - accuracy: 0.7409\nEpoch 26/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.5242 - accuracy: 0.7409\nEpoch 27/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.5197 - accuracy: 0.7458\nEpoch 28/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.5151 - accuracy: 0.7530\nEpoch 29/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.5106 - accuracy: 0.7603\nEpoch 30/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.5063 - accuracy: 0.7603\nEpoch 31/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.5014 - accuracy: 0.7651\nEpoch 32/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.4964 - accuracy: 0.7651\nEpoch 33/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.4912 - accuracy: 0.7748\nEpoch 34/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.4863 - accuracy: 0.7772\nEpoch 35/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.4813 - accuracy: 0.7797\nEpoch 36/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.4760 - accuracy: 0.7797\nEpoch 37/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.4708 - accuracy: 0.7869\nEpoch 38/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.4645 - accuracy: 0.7918\nEpoch 39/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.4588 - accuracy: 0.7942\nEpoch 40/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.4531 - accuracy: 0.7966\nEpoch 41/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.4474 - accuracy: 0.7966\nEpoch 42/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.4416 - accuracy: 0.8015\nEpoch 43/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.4359 - accuracy: 0.8015\nEpoch 44/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.4308 - accuracy: 0.8087\nEpoch 45/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.4254 - accuracy: 0.8111\nEpoch 46/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.4205 - accuracy: 0.8136\nEpoch 47/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.4150 - accuracy: 0.8208\nEpoch 48/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.4096 - accuracy: 0.8257\nEpoch 49/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.4052 - accuracy: 0.8257\nEpoch 50/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.3997 - accuracy: 0.8281\nEpoch 51/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.3951 - accuracy: 0.8305\nEpoch 52/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.3901 - accuracy: 0.8354\nEpoch 53/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.3854 - accuracy: 0.8426\nEpoch 54/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.3806 - accuracy: 0.8426\nEpoch 55/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.3757 - accuracy: 0.8450\nEpoch 56/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.3718 - accuracy: 0.8450\nEpoch 57/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.3673 - accuracy: 0.8475\nEpoch 58/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.3628 - accuracy: 0.8499\nEpoch 59/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.3584 - accuracy: 0.8523\nEpoch 60/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.3536 - accuracy: 0.8547\nEpoch 61/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.3495 - accuracy: 0.8547\nEpoch 62/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.3446 - accuracy: 0.8547\nEpoch 63/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.3401 - accuracy: 0.8547\nEpoch 64/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.3365 - accuracy: 0.8571\nEpoch 65/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.3314 - accuracy: 0.8620\nEpoch 66/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.3271 - accuracy: 0.8620\nEpoch 67/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.3233 - accuracy: 0.8596\nEpoch 68/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.3186 - accuracy: 0.8620\nEpoch 69/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.3142 - accuracy: 0.8620\nEpoch 70/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.3100 - accuracy: 0.8620\nEpoch 71/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.3050 - accuracy: 0.8644\nEpoch 72/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.3014 - accuracy: 0.8668\nEpoch 73/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.2980 - accuracy: 0.8668\nEpoch 74/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.2938 - accuracy: 0.8668\nEpoch 75/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.2903 - accuracy: 0.8668\nEpoch 76/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.2862 - accuracy: 0.8668\nEpoch 77/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.2821 - accuracy: 0.8692\nEpoch 78/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.2792 - accuracy: 0.8717\nEpoch 79/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.2749 - accuracy: 0.8717\nEpoch 80/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.2704 - accuracy: 0.8814\nEpoch 81/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.2659 - accuracy: 0.8814\nEpoch 82/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.2621 - accuracy: 0.8886\nEpoch 83/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.2585 - accuracy: 0.8935\nEpoch 84/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.2539 - accuracy: 0.8959\nEpoch 85/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.2511 - accuracy: 0.8959\nEpoch 86/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.2474 - accuracy: 0.8983\nEpoch 87/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.2430 - accuracy: 0.9007\nEpoch 88/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.2398 - accuracy: 0.9007\nEpoch 89/100\n13/13 [==============================] - 0s 5ms/step - loss: 0.2359 - accuracy: 0.9007\nEpoch 90/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.2327 - accuracy: 0.9007\nEpoch 91/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.2289 - accuracy: 0.9031\nEpoch 92/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.2258 - accuracy: 0.9031\nEpoch 93/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.2221 - accuracy: 0.9031\nEpoch 94/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.2189 - accuracy: 0.9056\nEpoch 95/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.2154 - accuracy: 0.9056\nEpoch 96/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.2132 - accuracy: 0.9128\nEpoch 97/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.2094 - accuracy: 0.9128\nEpoch 98/100\n13/13 [==============================] - 0s 4ms/step - loss: 0.2056 - accuracy: 0.9153\nEpoch 99/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.2023 - accuracy: 0.9201\nEpoch 100/100\n13/13 [==============================] - 0s 3ms/step - loss: 0.1992 - accuracy: 0.9153\n" | |
| }, | |
| { | |
| "data": { | |
| "text/plain": "<keras.callbacks.History at 0x229b5561640>" | |
| }, | |
| "execution_count": 23, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2023-07-19T04:07:40.239066Z", | |
| "start_time": "2023-07-19T04:07:39.949993Z" | |
| }, | |
| "trusted": false | |
| }, | |
| "id": "e3e67666", | |
| "cell_type": "code", | |
| "source": "# classification report\nfrom sklearn.metrics import classification_report\nthreshold = 0.50\nY_preds = np.where(model.predict(x_test)> threshold, 1, 0)\nprint(classification_report(y_test,Y_preds)) ", | |
| "execution_count": 24, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": "4/4 [==============================] - 0s 3ms/step\n precision recall f1-score support\n\n 0 0.89 0.95 0.92 76\n 1 0.83 0.68 0.75 28\n\n accuracy 0.88 104\n macro avg 0.86 0.81 0.83 104\nweighted avg 0.87 0.88 0.87 104\n\n" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "id": "e37aad5a", | |
| "cell_type": "code", | |
| "source": "", | |
| "execution_count": null, | |
| "outputs": [] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3 (ipykernel)", | |
| "language": "python" | |
| }, | |
| "language_info": { | |
| "name": "python", | |
| "version": "3.9.13", | |
| "mimetype": "text/x-python", | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "pygments_lexer": "ipython3", | |
| "nbconvert_exporter": "python", | |
| "file_extension": ".py" | |
| }, | |
| "gist": { | |
| "id": "", | |
| "data": { | |
| "description": "!excelR/assignments/Gists/ANN.ipynb", | |
| "public": true | |
| } | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 5 | |
| } |
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