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IOT Device Codev1
Commit adde9677 authored by Kethmini Yasara Karunarathne's avatar Kethmini Yasara Karunarathne 💪
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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "24CkKSrT6wOX",
"outputId": "3a23ffd0-fcf9-4013-c78f-65fbaab918da"
},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'google'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7644/1408506528.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdrive\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mdrive\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'/content/drive'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'google'"
]
}
],
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 589
},
"id": "lwaxgltpb_t1",
"outputId": "3fe671bd-cba2-4a4c-d992-9df33f027ab8"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000020263D74320>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/pmdarima/\n",
"WARNING: Retrying (Retry(total=3, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000020263D745F8>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/pmdarima/\n",
"WARNING: Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000020263D746D8>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/pmdarima/\n",
"WARNING: Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000020263D74828>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/pmdarima/\n",
"WARNING: Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x0000020263D74978>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')': /simple/pmdarima/\n",
"ERROR: Could not find a version that satisfies the requirement pmdarima (from versions: none)\n",
"ERROR: No matching distribution found for pmdarima\n"
]
}
],
"source": [
"!pip install pmdarima"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "NRi-Puy667s3"
},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'plotly'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7644/4142365750.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmath\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexpress\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;31m## for plotting\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'plotly'"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import math\n",
"import plotly.express as px\n",
"\n",
"## for plotting\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"## for statistical tests\n",
"import scipy\n",
"import statsmodels.formula.api as smf\n",
"import statsmodels.api as sm\n",
"## for machine learning\n",
"from sklearn import model_selection, preprocessing, feature_selection, ensemble, linear_model, metrics, decomposition\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "95WuxyEw7MzC"
},
"outputs": [],
"source": [
"df = pd.read_csv('/content/drive/MyDrive/Water_quality_prediction/water_potability.csv')\n",
"df=df.dropna()\n",
"df.reset_index(inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "9sHGF-d57SOp",
"outputId": "8e2c7a3e-950f-443e-df40-c88adf0e4432"
},
"outputs": [],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "D1Yajj_J7Uma",
"outputId": "d95ee06c-2589-401d-e499-ed74f91112e6"
},
"outputs": [],
"source": [
"df.describe()\n",
"size=len(df.index)\n",
"size"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qICc4KGyCfYm",
"outputId": "bbb31def-64a5-4a15-b35f-e4e1434cd638"
},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn import metrics\n",
"from sklearn.model_selection import train_test_split\n",
" \n",
"X= df.drop(['index','Potability'],axis=1)\n",
"y= df['Potability']\n",
"classifier = RandomForestClassifier()\n",
"\n",
"#creating a train-test split with a proportion of 70:30\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)\n",
" \n",
"classifier.fit(X_train, y_train) #train the classifier on the training set\n",
" \n",
"y_pred = classifier.predict(X_test) #evaluate the classifier on unknown data\n",
"\n",
"print(\"Accuracy: \", metrics.accuracy_score(y_test, y_pred))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "_8bIE4YK9PzC",
"outputId": "43793a18-7f2d-4fb2-f791-5abca9702b91"
},
"outputs": [],
"source": [
"\n",
"fig0 = px.line(df, x=np.arange(size),y='ph')\n",
"fig0.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "CFuucifa7Ydd",
"outputId": "78601046-9f3f-4566-fb7f-e7916a1d627e"
},
"outputs": [],
"source": [
"fig1 = px.line(df, x=np.arange(size),y='TDS')\n",
"fig1.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "m7FnN-Tt8Ikj",
"outputId": "a26612ee-5f68-4b54-a90a-0759d5950e20"
},
"outputs": [],
"source": [
"fig2 = px.line(df, x=np.arange(size),y='Turbidity')\n",
"fig2.show()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "n_YzbZcyJQOC",
"outputId": "1c7031ac-acfe-458a-ccbd-834afd4f69db"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'px' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7644/754512660.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfig3\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Potability'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mfig3\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'px' is not defined"
]
}
],
"source": [
"fig3 = px.line(df, x=np.arange(size),y='Potability')\n",
"fig3.show()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ssdqdxAig0iQ",
"outputId": "bae3ec1f-beb3-436b-9f49-f79890acf47c"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'df' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7644/960605004.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mtest\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
]
}
],
"source": [
"print(df.shape)\n",
"train=df.iloc[:-50]\n",
"test=df.iloc[-50:]\n",
"print(train.shape,test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 545
},
"id": "msbb5auoTU9M",
"outputId": "51b4f4f3-295b-46d1-f8ae-b6ff25c4b8e5"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'train' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7644/699948010.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mstatsmodels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgraphics\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtsaplots\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mplot_acf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplot_pacf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0macf_original\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot_acf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'ph'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mpacf_original\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot_pacf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'ph'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'train' is not defined"
]
}
],
"source": [
"from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
"\n",
"acf_original = plot_acf(train['ph'])\n",
"\n",
"pacf_original = plot_pacf(train['ph'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oRATzf-wfRrw"
},
"source": [
"If p< 0.05 ; Data is stationary\n",
"\n",
"if p>0.05; Data is not stationary"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "RecKdt1ZWxPI",
"outputId": "a90124c4-2952-4a90-eb72-eff0b7e96e1b"
},
"outputs": [],
"source": [
"from statsmodels.tsa.stattools import adfuller\n",
"adf_test = adfuller(train['ph'])\n",
"print(f'p-value - ph: {adf_test[1]}')\n",
"adf_test = adfuller(train['TDS'])\n",
"print(f'p-value - TDS: {adf_test[1]}')\n",
"adf_test = adfuller(train['Turbidity'])\n",
"print(f'p-value - Turbidity: {adf_test[1]}')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9Uf5ct1veBaQ"
},
"outputs": [],
"source": [
"\n",
"def adf_test(dataset):\n",
" dftest = adfuller(dataset, autolag = 'AIC')\n",
" print(\"1. ADF : \",dftest[0])\n",
" print(\"2. P-Value : \", dftest[1])\n",
" print(\"3. Num Of Lags : \", dftest[2])\n",
" print(\"4. Num Of Observations Used For ADF Regression:\", dftest[3])\n",
" print(\"5. Critical Values :\")\n",
" for key, val in dftest[4].items():\n",
" print(\"\\t\",key, \": \", val)\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "t5rqC8vQExyA",
"outputId": "82fd69aa-ec35-49cd-8a56-e4912583367b"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'adf_test' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7644/1699789831.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0madf_test\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'ph'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'adf_test' is not defined"
]
}
],
"source": [
"adf_test(df['ph'])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-KWXX4-MfCBu",
"outputId": "32fcf187-a035-482b-fa24-f9d5605c8e72"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'adf_test' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7644/3888870137.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0madf_test\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'TDS'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'adf_test' is not defined"
]
}
],
"source": [
"adf_test(df['TDS'])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "h-4dCby0fG6g",
"outputId": "ac2753e5-da12-4e7e-8862-794690cbbcef"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'adf_test' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7644/3820446633.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0madf_test\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Turbidity'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'adf_test' is not defined"
]
}
],
"source": [
"adf_test(df['Turbidity'])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8Uf3rIImjrVG",
"outputId": "aff6cfef-9388-4f3d-d71c-a23beb5f18e7"
},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'pmdarima.utils.array'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7644/139859592.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mpmdarima\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mauto_arima\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m stepwise_fit = auto_arima(df['ph'], trace=True,\n\u001b[0;32m 3\u001b[0m suppress_warnings=True)\n",
"\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\pmdarima\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 51\u001b[0m \u001b[1;31m# Stuff we want at top-level\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 52\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0marima\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mauto_arima\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mARIMA\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mAutoARIMA\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mStepwiseContext\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdecompose\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 53\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0macf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mautocorr_plot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpacf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplot_acf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplot_pacf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 54\u001b[0m \u001b[0mtsdisplay\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\pmdarima\\arima\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;31m# Author: Taylor Smith <taylor.smith@alkaline-ml.com>\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mapprox\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 6\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0marima\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mauto\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\pmdarima\\arima\\approx.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_endog\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 10\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mget_callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumpy\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mDTYPE\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\pmdarima\\utils\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;31m# Author: Taylor Smith <taylor.smith@alkaline-ml.com>\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 6\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mmetaestimators\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mvisualization\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'pmdarima.utils.array'"
]
}
],
"source": [
"from pmdarima import auto_arima\n",
"stepwise_fit = auto_arima(df['ph'], trace=True,\n",
"suppress_warnings=True)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 494
},
"id": "wWMGjNT2j8Nm",
"outputId": "3694e176-9905-4008-a83b-ec5627a92b90"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'train' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7644/463776756.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mstatsmodels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtsa\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marima\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mARIMA\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mARIMA\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'ph'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'train' is not defined"
]
}
],
"source": [
"from statsmodels.tsa.arima.model import ARIMA\n",
"model=ARIMA(train['ph'],order=(2,0,2))\n",
"model=model.fit()\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 281
},
"id": "8McO4U1UYSeF",
"outputId": "ab9ac15d-d9b7-4f70-e989-fc9d3942f701"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'model' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7644/1068234452.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mresiduals\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresid\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mresiduals\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Residuals'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mresiduals\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Density'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'kde'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'model' is not defined"
]
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"residuals = model.resid[1:]\n",
"fig, ax = plt.subplots(1,2)\n",
"residuals.plot(title='Residuals', ax=ax[0])\n",
"residuals.plot(title='Density', kind='kde', ax=ax[1])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
},
"id": "cRWnEhxlYbGA",
"outputId": "eaeac7cc-ba10-4a83-b1c1-517a010ed1f4"
},
"outputs": [],
"source": [
"forecast_test_ph = model.forecast(len(test))\n",
"forecast_test_ph.plot()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"id": "azGCnae4kJXp"
},
"outputs": [],
"source": [
"# start=len(train)\n",
"# end=len(train)+len(test)-1\n",
"# pred1=model.predict(start=start,end=end,typ='levels').rename('ARIMA Predictions')\n",
"# pred1.plot(legend=True)\n",
"# test['ph'].plot(legend=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "EhaSOk-OgCMh",
"outputId": "3157ec1c-6797-41a8-fa60-cd322230f192"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'auto_arima' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7644/2326555360.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m stepwise_fit = auto_arima(df['TDS'], trace=True,\n\u001b[0m\u001b[0;32m 2\u001b[0m suppress_warnings=True)\n",
"\u001b[1;31mNameError\u001b[0m: name 'auto_arima' is not defined"
]
}
],
"source": [
"\n",
"stepwise_fit = auto_arima(df['TDS'], trace=True,\n",
"suppress_warnings=True)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 448
},
"id": "MLyXfaXwhCe9",
"outputId": "5e643fb0-e183-4b74-dd58-1aece53d2476"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'train' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7644/2456783757.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mstatsmodels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtsa\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marima\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mARIMA\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mARIMA\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'TDS'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'train' is not defined"
]
}
],
"source": [
"from statsmodels.tsa.arima.model import ARIMA\n",
"model=ARIMA(train['TDS'],order=(1,0,0))\n",
"model=model.fit()\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
},
"id": "mGTWBTUEaggY",
"outputId": "dc60da6a-14e8-4000-d0ec-4039e1372fe5"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'model' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7644/77346384.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mforecast_test_tds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforecast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mforecast_test_tds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'model' is not defined"
]
}
],
"source": [
"forecast_test_tds = model.forecast(len(test))\n",
"forecast_test_tds.plot()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"id": "iz6fDcDohmoW"
},
"outputs": [],
"source": [
"# start=len(train)\n",
"# end=len(train)+len(test)-1\n",
"# pred2=model.predict(start=start,end=end,typ='levels').rename('ARIMA Predictions')\n",
"# pred2.plot(legend=True)\n",
"# test['TDS'].plot(legend=True)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8lSSbrR8hxp-",
"outputId": "c3966b10-d150-4523-81ea-4301ba7aa975"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'test' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7644/698249490.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmean_squared_error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmath\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msqrt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mtest\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'TDS'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0mrmse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msqrt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmean_squared_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mforecast_test_tds\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'TDS'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrmse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'test' is not defined"
]
}
],
"source": [
"from sklearn.metrics import mean_squared_error\n",
"from math import sqrt\n",
"test['TDS'].mean()\n",
"rmse=sqrt(mean_squared_error(forecast_test_tds,test['TDS']))\n",
"print(rmse)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "MbK_tnFCnOQE",
"outputId": "e9ac19e5-2d4b-4cff-e72e-02871c75885a"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'auto_arima' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7644/2411118502.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m stepwise_fit = auto_arima(df['Turbidity'], trace=True,\n\u001b[0m\u001b[0;32m 2\u001b[0m suppress_warnings=True)\n",
"\u001b[1;31mNameError\u001b[0m: name 'auto_arima' is not defined"
]
}
],
"source": [
"stepwise_fit = auto_arima(df['Turbidity'], trace=True,\n",
"suppress_warnings=True)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 409
},
"id": "WVqGA17bmx8q",
"outputId": "61fbb201-3cfa-4c0a-c478-0acab71ebca3"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'train' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7644/1804235771.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mstatsmodels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtsa\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marima\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mARIMA\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mARIMA\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Turbidity'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'train' is not defined"
]
}
],
"source": [
"from statsmodels.tsa.arima.model import ARIMA\n",
"model=ARIMA(train['Turbidity'],order=(0,0,0))\n",
"model=model.fit()\n",
"model.summary()\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
},
"id": "nnGAN6WhbBaf",
"outputId": "b553fce4-b1bd-4d60-f830-9f2acb135a18"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'model' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7644/2737279083.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mforecast_test_Turbidity\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforecast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mforecast_test_Turbidity\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'model' is not defined"
]
}
],
"source": [
"forecast_test_Turbidity = model.forecast(len(test))\n",
"forecast_test_Turbidity.plot()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"id": "nOlhm2fCa7vA"
},
"outputs": [],
"source": [
"# start=len(train)\n",
"# end=len(train)+len(test)-1\n",
"# pred3=model.predict(start=start,end=end,typ='levels').rename('ARIMA Predictions')\n",
"# pred3.plot(legend=True)\n",
"# test['Turbidity'].plot(legend=True)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "FTO0eI3DGSgZ",
"outputId": "91a7b338-027f-4d0c-eff8-d634daf351d3"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'forecast_test_ph' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7644/2239525783.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m df_pred = pd.DataFrame(list(zip(forecast_test_ph, forecast_test_tds, forecast_test_Turbidity)),\n\u001b[0m\u001b[0;32m 2\u001b[0m columns =['ph', 'TDS', 'Turbidity'])\n\u001b[0;32m 3\u001b[0m \u001b[0mdf_pred\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'forecast_test_ph' is not defined"
]
}
],
"source": [
"df_pred = pd.DataFrame(list(zip(forecast_test_ph, forecast_test_tds, forecast_test_Turbidity)),\n",
" columns =['ph', 'TDS', 'Turbidity'])\n",
"df_pred"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zZmIx2FWGxHz",
"outputId": "fab94b90-cbdf-4fb5-9dd6-88485455aafc"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'classifier' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_7644/3770677011.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0my_pred_forecast\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_pred\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#evaluate the classifier on unknown data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_pred_forecast\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'classifier' is not defined"
]
}
],
"source": [
"y_pred_forecast = classifier.predict(df_pred) #evaluate the classifier on unknown data\n",
"\n",
"print(y_pred_forecast)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
\ No newline at end of file
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