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April 5, 2021 15:58
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AIRLINE PASSENGERS FORECASTING.ipynb
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| { | |
| "cells": [ | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "import pandas as pd\nimport numpy as np\n", | |
| "execution_count": 1, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "data=pd.read_excel(\"Airlines+Data.xlsx\")", | |
| "execution_count": 2, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "data", | |
| "execution_count": 3, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 3, | |
| "data": { | |
| "text/plain": " Month Passengers\n0 1995-01-01 112\n1 1995-02-01 118\n2 1995-03-01 132\n3 1995-04-01 129\n4 1995-05-01 121\n.. ... ...\n91 2002-08-01 405\n92 2002-09-01 355\n93 2002-10-01 306\n94 2002-11-01 271\n95 2002-12-01 306\n\n[96 rows x 2 columns]", | |
| "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>Month</th>\n <th>Passengers</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1995-01-01</td>\n <td>112</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1995-02-01</td>\n <td>118</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1995-03-01</td>\n <td>132</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1995-04-01</td>\n <td>129</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1995-05-01</td>\n <td>121</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>91</th>\n <td>2002-08-01</td>\n <td>405</td>\n </tr>\n <tr>\n <th>92</th>\n <td>2002-09-01</td>\n <td>355</td>\n </tr>\n <tr>\n <th>93</th>\n <td>2002-10-01</td>\n <td>306</td>\n </tr>\n <tr>\n <th>94</th>\n <td>2002-11-01</td>\n <td>271</td>\n </tr>\n <tr>\n <th>95</th>\n <td>2002-12-01</td>\n <td>306</td>\n </tr>\n </tbody>\n</table>\n<p>96 rows × 2 columns</p>\n</div>" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "data[\"Date\"] = pd.to_datetime(data.Month,format=\"%b-%y\")\n#look for c standard format codes\n\n# Extracting Day, weekday name, month name, year from the Date column using \n# Date functions from pandas \n\ndata[\"month\"] = data.Date.dt.strftime(\"%b\") # month extraction\ndata[\"year\"] = data.Date.dt.strftime(\"%Y\") # year extraction\n\n#Walmart[\"Day\"] = Walmart.Date.dt.strftime(\"%d\") # Day extraction\n#Walmart[\"wkday\"] = Walmart.Date.dt.strftime(\"%A\") # weekday extraction", | |
| "execution_count": 4, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "month =['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'] \n", | |
| "execution_count": 5, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "month_dummies = pd.DataFrame(pd.get_dummies(data['month']))\n", | |
| "execution_count": 6, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "month_dummies", | |
| "execution_count": 7, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 7, | |
| "data": { | |
| "text/plain": " Apr Aug Dec Feb Jan Jul Jun Mar May Nov Oct Sep\n0 0 0 0 0 1 0 0 0 0 0 0 0\n1 0 0 0 1 0 0 0 0 0 0 0 0\n2 0 0 0 0 0 0 0 1 0 0 0 0\n3 1 0 0 0 0 0 0 0 0 0 0 0\n4 0 0 0 0 0 0 0 0 1 0 0 0\n.. ... ... ... ... ... ... ... ... ... ... ... ...\n91 0 1 0 0 0 0 0 0 0 0 0 0\n92 0 0 0 0 0 0 0 0 0 0 0 1\n93 0 0 0 0 0 0 0 0 0 0 1 0\n94 0 0 0 0 0 0 0 0 0 1 0 0\n95 0 0 1 0 0 0 0 0 0 0 0 0\n\n[96 rows x 12 columns]", | |
| "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>Apr</th>\n <th>Aug</th>\n <th>Dec</th>\n <th>Feb</th>\n <th>Jan</th>\n <th>Jul</th>\n <th>Jun</th>\n <th>Mar</th>\n <th>May</th>\n <th>Nov</th>\n <th>Oct</th>\n <th>Sep</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\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 <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\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 <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>2</th>\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 <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1</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 <td>0</td>\n </tr>\n <tr>\n <th>4</th>\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 <td>0</td>\n <td>0</td>\n <td>0</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 <td>...</td>\n </tr>\n <tr>\n <th>91</th>\n <td>0</td>\n <td>1</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>92</th>\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 <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>93</th>\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 <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>94</th>\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 <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>95</th>\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 <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 </tbody>\n</table>\n<p>96 rows × 12 columns</p>\n</div>" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df = pd.concat([data,month_dummies],axis = 1)", | |
| "execution_count": 8, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df", | |
| "execution_count": 9, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 9, | |
| "data": { | |
| "text/plain": " Month Passengers Date month year Apr Aug Dec Feb Jan \\\n0 1995-01-01 112 1995-01-01 Jan 1995 0 0 0 0 1 \n1 1995-02-01 118 1995-02-01 Feb 1995 0 0 0 1 0 \n2 1995-03-01 132 1995-03-01 Mar 1995 0 0 0 0 0 \n3 1995-04-01 129 1995-04-01 Apr 1995 1 0 0 0 0 \n4 1995-05-01 121 1995-05-01 May 1995 0 0 0 0 0 \n.. ... ... ... ... ... ... ... ... ... ... \n91 2002-08-01 405 2002-08-01 Aug 2002 0 1 0 0 0 \n92 2002-09-01 355 2002-09-01 Sep 2002 0 0 0 0 0 \n93 2002-10-01 306 2002-10-01 Oct 2002 0 0 0 0 0 \n94 2002-11-01 271 2002-11-01 Nov 2002 0 0 0 0 0 \n95 2002-12-01 306 2002-12-01 Dec 2002 0 0 1 0 0 \n\n Jul Jun Mar May Nov Oct Sep \n0 0 0 0 0 0 0 0 \n1 0 0 0 0 0 0 0 \n2 0 0 1 0 0 0 0 \n3 0 0 0 0 0 0 0 \n4 0 0 0 1 0 0 0 \n.. ... ... ... ... ... ... ... \n91 0 0 0 0 0 0 0 \n92 0 0 0 0 0 0 1 \n93 0 0 0 0 0 1 0 \n94 0 0 0 0 1 0 0 \n95 0 0 0 0 0 0 0 \n\n[96 rows x 17 columns]", | |
| "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>Month</th>\n <th>Passengers</th>\n <th>Date</th>\n <th>month</th>\n <th>year</th>\n <th>Apr</th>\n <th>Aug</th>\n <th>Dec</th>\n <th>Feb</th>\n <th>Jan</th>\n <th>Jul</th>\n <th>Jun</th>\n <th>Mar</th>\n <th>May</th>\n <th>Nov</th>\n <th>Oct</th>\n <th>Sep</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1995-01-01</td>\n <td>112</td>\n <td>1995-01-01</td>\n <td>Jan</td>\n <td>1995</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 <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1995-02-01</td>\n <td>118</td>\n <td>1995-02-01</td>\n <td>Feb</td>\n <td>1995</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 <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>2</th>\n <td>1995-03-01</td>\n <td>132</td>\n <td>1995-03-01</td>\n <td>Mar</td>\n <td>1995</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 <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1995-04-01</td>\n <td>129</td>\n <td>1995-04-01</td>\n <td>Apr</td>\n <td>1995</td>\n <td>1</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 <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1995-05-01</td>\n <td>121</td>\n <td>1995-05-01</td>\n <td>May</td>\n <td>1995</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 <td>0</td>\n <td>0</td>\n <td>0</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 <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>91</th>\n <td>2002-08-01</td>\n <td>405</td>\n <td>2002-08-01</td>\n <td>Aug</td>\n <td>2002</td>\n <td>0</td>\n <td>1</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>92</th>\n <td>2002-09-01</td>\n <td>355</td>\n <td>2002-09-01</td>\n <td>Sep</td>\n <td>2002</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 <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>93</th>\n <td>2002-10-01</td>\n <td>306</td>\n <td>2002-10-01</td>\n <td>Oct</td>\n <td>2002</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 <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>94</th>\n <td>2002-11-01</td>\n <td>271</td>\n <td>2002-11-01</td>\n <td>Nov</td>\n <td>2002</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 <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>95</th>\n <td>2002-12-01</td>\n <td>306</td>\n <td>2002-12-01</td>\n <td>Dec</td>\n <td>2002</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 <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 </tbody>\n</table>\n<p>96 rows × 17 columns</p>\n</div>" | |
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| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df[\"t\"] = np.arange(1,97)\n\ndf[\"t_squared\"] = df[\"t\"]*df[\"t\"]\ndf.columns\ndf[\"log_Passengers\"] = np.log(df[\"Passengers\"])", | |
| "execution_count": 10, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df", | |
| "execution_count": 11, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 11, | |
| "data": { | |
| "text/plain": " Month Passengers Date month year Apr Aug Dec Feb Jan \\\n0 1995-01-01 112 1995-01-01 Jan 1995 0 0 0 0 1 \n1 1995-02-01 118 1995-02-01 Feb 1995 0 0 0 1 0 \n2 1995-03-01 132 1995-03-01 Mar 1995 0 0 0 0 0 \n3 1995-04-01 129 1995-04-01 Apr 1995 1 0 0 0 0 \n4 1995-05-01 121 1995-05-01 May 1995 0 0 0 0 0 \n.. ... ... ... ... ... ... ... ... ... ... \n91 2002-08-01 405 2002-08-01 Aug 2002 0 1 0 0 0 \n92 2002-09-01 355 2002-09-01 Sep 2002 0 0 0 0 0 \n93 2002-10-01 306 2002-10-01 Oct 2002 0 0 0 0 0 \n94 2002-11-01 271 2002-11-01 Nov 2002 0 0 0 0 0 \n95 2002-12-01 306 2002-12-01 Dec 2002 0 0 1 0 0 \n\n Jul Jun Mar May Nov Oct Sep t t_squared log_Passengers \n0 0 0 0 0 0 0 0 1 1 4.718499 \n1 0 0 0 0 0 0 0 2 4 4.770685 \n2 0 0 1 0 0 0 0 3 9 4.882802 \n3 0 0 0 0 0 0 0 4 16 4.859812 \n4 0 0 0 1 0 0 0 5 25 4.795791 \n.. ... ... ... ... ... ... ... .. ... ... \n91 0 0 0 0 0 0 0 92 8464 6.003887 \n92 0 0 0 0 0 0 1 93 8649 5.872118 \n93 0 0 0 0 0 1 0 94 8836 5.723585 \n94 0 0 0 0 1 0 0 95 9025 5.602119 \n95 0 0 0 0 0 0 0 96 9216 5.723585 \n\n[96 rows x 20 columns]", | |
| "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>Month</th>\n <th>Passengers</th>\n <th>Date</th>\n <th>month</th>\n <th>year</th>\n <th>Apr</th>\n <th>Aug</th>\n <th>Dec</th>\n <th>Feb</th>\n <th>Jan</th>\n <th>Jul</th>\n <th>Jun</th>\n <th>Mar</th>\n <th>May</th>\n <th>Nov</th>\n <th>Oct</th>\n <th>Sep</th>\n <th>t</th>\n <th>t_squared</th>\n <th>log_Passengers</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1995-01-01</td>\n <td>112</td>\n <td>1995-01-01</td>\n <td>Jan</td>\n <td>1995</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 <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>4.718499</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1995-02-01</td>\n <td>118</td>\n <td>1995-02-01</td>\n <td>Feb</td>\n <td>1995</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 <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>2</td>\n <td>4</td>\n <td>4.770685</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1995-03-01</td>\n <td>132</td>\n <td>1995-03-01</td>\n <td>Mar</td>\n <td>1995</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 <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>3</td>\n <td>9</td>\n <td>4.882802</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1995-04-01</td>\n <td>129</td>\n <td>1995-04-01</td>\n <td>Apr</td>\n <td>1995</td>\n <td>1</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 <td>0</td>\n <td>4</td>\n <td>16</td>\n <td>4.859812</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1995-05-01</td>\n <td>121</td>\n <td>1995-05-01</td>\n <td>May</td>\n <td>1995</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 <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>5</td>\n <td>25</td>\n <td>4.795791</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 <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>91</th>\n <td>2002-08-01</td>\n <td>405</td>\n <td>2002-08-01</td>\n <td>Aug</td>\n <td>2002</td>\n <td>0</td>\n <td>1</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 <td>92</td>\n <td>8464</td>\n <td>6.003887</td>\n </tr>\n <tr>\n <th>92</th>\n <td>2002-09-01</td>\n <td>355</td>\n <td>2002-09-01</td>\n <td>Sep</td>\n <td>2002</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 <td>0</td>\n <td>1</td>\n <td>93</td>\n <td>8649</td>\n <td>5.872118</td>\n </tr>\n <tr>\n <th>93</th>\n <td>2002-10-01</td>\n <td>306</td>\n <td>2002-10-01</td>\n <td>Oct</td>\n <td>2002</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 <td>1</td>\n <td>0</td>\n <td>94</td>\n <td>8836</td>\n <td>5.723585</td>\n </tr>\n <tr>\n <th>94</th>\n <td>2002-11-01</td>\n <td>271</td>\n <td>2002-11-01</td>\n <td>Nov</td>\n <td>2002</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 <td>0</td>\n <td>0</td>\n <td>95</td>\n <td>9025</td>\n <td>5.602119</td>\n </tr>\n <tr>\n <th>95</th>\n <td>2002-12-01</td>\n <td>306</td>\n <td>2002-12-01</td>\n <td>Dec</td>\n <td>2002</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 <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>96</td>\n <td>9216</td>\n <td>5.723585</td>\n </tr>\n </tbody>\n</table>\n<p>96 rows × 20 columns</p>\n</div>" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "Train = df.head(84)\nTest = df.tail(12)", | |
| "execution_count": 12, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "Train", | |
| "execution_count": 13, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 13, | |
| "data": { | |
| "text/plain": " Month Passengers Date month year Apr Aug Dec Feb Jan \\\n0 1995-01-01 112 1995-01-01 Jan 1995 0 0 0 0 1 \n1 1995-02-01 118 1995-02-01 Feb 1995 0 0 0 1 0 \n2 1995-03-01 132 1995-03-01 Mar 1995 0 0 0 0 0 \n3 1995-04-01 129 1995-04-01 Apr 1995 1 0 0 0 0 \n4 1995-05-01 121 1995-05-01 May 1995 0 0 0 0 0 \n.. ... ... ... ... ... ... ... ... ... ... \n79 2001-08-01 347 2001-08-01 Aug 2001 0 1 0 0 0 \n80 2001-09-01 312 2001-09-01 Sep 2001 0 0 0 0 0 \n81 2001-10-01 274 2001-10-01 Oct 2001 0 0 0 0 0 \n82 2001-11-01 237 2001-11-01 Nov 2001 0 0 0 0 0 \n83 2001-12-01 278 2001-12-01 Dec 2001 0 0 1 0 0 \n\n Jul Jun Mar May Nov Oct Sep t t_squared log_Passengers \n0 0 0 0 0 0 0 0 1 1 4.718499 \n1 0 0 0 0 0 0 0 2 4 4.770685 \n2 0 0 1 0 0 0 0 3 9 4.882802 \n3 0 0 0 0 0 0 0 4 16 4.859812 \n4 0 0 0 1 0 0 0 5 25 4.795791 \n.. ... ... ... ... ... ... ... .. ... ... \n79 0 0 0 0 0 0 0 80 6400 5.849325 \n80 0 0 0 0 0 0 1 81 6561 5.743003 \n81 0 0 0 0 0 1 0 82 6724 5.613128 \n82 0 0 0 0 1 0 0 83 6889 5.468060 \n83 0 0 0 0 0 0 0 84 7056 5.627621 \n\n[84 rows x 20 columns]", | |
| "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>Month</th>\n <th>Passengers</th>\n <th>Date</th>\n <th>month</th>\n <th>year</th>\n <th>Apr</th>\n <th>Aug</th>\n <th>Dec</th>\n <th>Feb</th>\n <th>Jan</th>\n <th>Jul</th>\n <th>Jun</th>\n <th>Mar</th>\n <th>May</th>\n <th>Nov</th>\n <th>Oct</th>\n <th>Sep</th>\n <th>t</th>\n <th>t_squared</th>\n <th>log_Passengers</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1995-01-01</td>\n <td>112</td>\n <td>1995-01-01</td>\n <td>Jan</td>\n <td>1995</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 <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>4.718499</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1995-02-01</td>\n <td>118</td>\n <td>1995-02-01</td>\n <td>Feb</td>\n <td>1995</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 <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>2</td>\n <td>4</td>\n <td>4.770685</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1995-03-01</td>\n <td>132</td>\n <td>1995-03-01</td>\n <td>Mar</td>\n <td>1995</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 <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>3</td>\n <td>9</td>\n <td>4.882802</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1995-04-01</td>\n <td>129</td>\n <td>1995-04-01</td>\n <td>Apr</td>\n <td>1995</td>\n <td>1</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 <td>0</td>\n <td>4</td>\n <td>16</td>\n <td>4.859812</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1995-05-01</td>\n <td>121</td>\n <td>1995-05-01</td>\n <td>May</td>\n <td>1995</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 <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>5</td>\n <td>25</td>\n <td>4.795791</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 <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>79</th>\n <td>2001-08-01</td>\n <td>347</td>\n <td>2001-08-01</td>\n <td>Aug</td>\n <td>2001</td>\n <td>0</td>\n <td>1</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 <td>80</td>\n <td>6400</td>\n <td>5.849325</td>\n </tr>\n <tr>\n <th>80</th>\n <td>2001-09-01</td>\n <td>312</td>\n <td>2001-09-01</td>\n <td>Sep</td>\n <td>2001</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 <td>0</td>\n <td>1</td>\n <td>81</td>\n <td>6561</td>\n <td>5.743003</td>\n </tr>\n <tr>\n <th>81</th>\n <td>2001-10-01</td>\n <td>274</td>\n <td>2001-10-01</td>\n <td>Oct</td>\n <td>2001</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 <td>1</td>\n <td>0</td>\n <td>82</td>\n <td>6724</td>\n <td>5.613128</td>\n </tr>\n <tr>\n <th>82</th>\n <td>2001-11-01</td>\n <td>237</td>\n <td>2001-11-01</td>\n <td>Nov</td>\n <td>2001</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 <td>0</td>\n <td>0</td>\n <td>83</td>\n <td>6889</td>\n <td>5.468060</td>\n </tr>\n <tr>\n <th>83</th>\n <td>2001-12-01</td>\n <td>278</td>\n <td>2001-12-01</td>\n <td>Dec</td>\n <td>2001</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 <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>84</td>\n <td>7056</td>\n <td>5.627621</td>\n </tr>\n </tbody>\n</table>\n<p>84 rows × 20 columns</p>\n</div>" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "scrolled": true, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "Test", | |
| "execution_count": 14, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 14, | |
| "data": { | |
| "text/plain": " Month Passengers Date month year Apr Aug Dec Feb Jan \\\n84 2002-01-01 284 2002-01-01 Jan 2002 0 0 0 0 1 \n85 2002-02-01 277 2002-02-01 Feb 2002 0 0 0 1 0 \n86 2002-03-01 317 2002-03-01 Mar 2002 0 0 0 0 0 \n87 2002-04-01 313 2002-04-01 Apr 2002 1 0 0 0 0 \n88 2002-05-01 318 2002-05-01 May 2002 0 0 0 0 0 \n89 2002-06-01 374 2002-06-01 Jun 2002 0 0 0 0 0 \n90 2002-07-01 413 2002-07-01 Jul 2002 0 0 0 0 0 \n91 2002-08-01 405 2002-08-01 Aug 2002 0 1 0 0 0 \n92 2002-09-01 355 2002-09-01 Sep 2002 0 0 0 0 0 \n93 2002-10-01 306 2002-10-01 Oct 2002 0 0 0 0 0 \n94 2002-11-01 271 2002-11-01 Nov 2002 0 0 0 0 0 \n95 2002-12-01 306 2002-12-01 Dec 2002 0 0 1 0 0 \n\n Jul Jun Mar May Nov Oct Sep t t_squared log_Passengers \n84 0 0 0 0 0 0 0 85 7225 5.648974 \n85 0 0 0 0 0 0 0 86 7396 5.624018 \n86 0 0 1 0 0 0 0 87 7569 5.758902 \n87 0 0 0 0 0 0 0 88 7744 5.746203 \n88 0 0 0 1 0 0 0 89 7921 5.762051 \n89 0 1 0 0 0 0 0 90 8100 5.924256 \n90 1 0 0 0 0 0 0 91 8281 6.023448 \n91 0 0 0 0 0 0 0 92 8464 6.003887 \n92 0 0 0 0 0 0 1 93 8649 5.872118 \n93 0 0 0 0 0 1 0 94 8836 5.723585 \n94 0 0 0 0 1 0 0 95 9025 5.602119 \n95 0 0 0 0 0 0 0 96 9216 5.723585 ", | |
| "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>Month</th>\n <th>Passengers</th>\n <th>Date</th>\n <th>month</th>\n <th>year</th>\n <th>Apr</th>\n <th>Aug</th>\n <th>Dec</th>\n <th>Feb</th>\n <th>Jan</th>\n <th>Jul</th>\n <th>Jun</th>\n <th>Mar</th>\n <th>May</th>\n <th>Nov</th>\n <th>Oct</th>\n <th>Sep</th>\n <th>t</th>\n <th>t_squared</th>\n <th>log_Passengers</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>84</th>\n <td>2002-01-01</td>\n <td>284</td>\n <td>2002-01-01</td>\n <td>Jan</td>\n <td>2002</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 <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>85</td>\n <td>7225</td>\n <td>5.648974</td>\n </tr>\n <tr>\n <th>85</th>\n <td>2002-02-01</td>\n <td>277</td>\n <td>2002-02-01</td>\n <td>Feb</td>\n <td>2002</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 <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>86</td>\n <td>7396</td>\n <td>5.624018</td>\n </tr>\n <tr>\n <th>86</th>\n <td>2002-03-01</td>\n <td>317</td>\n <td>2002-03-01</td>\n <td>Mar</td>\n <td>2002</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 <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>87</td>\n <td>7569</td>\n <td>5.758902</td>\n </tr>\n <tr>\n <th>87</th>\n <td>2002-04-01</td>\n <td>313</td>\n <td>2002-04-01</td>\n <td>Apr</td>\n <td>2002</td>\n <td>1</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 <td>0</td>\n <td>88</td>\n <td>7744</td>\n <td>5.746203</td>\n </tr>\n <tr>\n <th>88</th>\n <td>2002-05-01</td>\n <td>318</td>\n <td>2002-05-01</td>\n <td>May</td>\n <td>2002</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 <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>89</td>\n <td>7921</td>\n <td>5.762051</td>\n </tr>\n <tr>\n <th>89</th>\n <td>2002-06-01</td>\n <td>374</td>\n <td>2002-06-01</td>\n <td>Jun</td>\n <td>2002</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 <td>0</td>\n <td>0</td>\n <td>90</td>\n <td>8100</td>\n <td>5.924256</td>\n </tr>\n <tr>\n <th>90</th>\n <td>2002-07-01</td>\n <td>413</td>\n <td>2002-07-01</td>\n <td>Jul</td>\n <td>2002</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 <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>91</td>\n <td>8281</td>\n <td>6.023448</td>\n </tr>\n <tr>\n <th>91</th>\n <td>2002-08-01</td>\n <td>405</td>\n <td>2002-08-01</td>\n <td>Aug</td>\n <td>2002</td>\n <td>0</td>\n <td>1</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 <td>92</td>\n <td>8464</td>\n <td>6.003887</td>\n </tr>\n <tr>\n <th>92</th>\n <td>2002-09-01</td>\n <td>355</td>\n <td>2002-09-01</td>\n <td>Sep</td>\n <td>2002</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 <td>0</td>\n <td>1</td>\n <td>93</td>\n <td>8649</td>\n <td>5.872118</td>\n </tr>\n <tr>\n <th>93</th>\n <td>2002-10-01</td>\n <td>306</td>\n <td>2002-10-01</td>\n <td>Oct</td>\n <td>2002</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 <td>1</td>\n <td>0</td>\n <td>94</td>\n <td>8836</td>\n <td>5.723585</td>\n </tr>\n <tr>\n <th>94</th>\n <td>2002-11-01</td>\n <td>271</td>\n <td>2002-11-01</td>\n <td>Nov</td>\n <td>2002</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 <td>0</td>\n <td>0</td>\n <td>95</td>\n <td>9025</td>\n <td>5.602119</td>\n </tr>\n <tr>\n <th>95</th>\n <td>2002-12-01</td>\n <td>306</td>\n <td>2002-12-01</td>\n <td>Dec</td>\n <td>2002</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 <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>96</td>\n <td>9216</td>\n <td>5.723585</td>\n </tr>\n </tbody>\n</table>\n</div>" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df.Passengers.plot()", | |
| "execution_count": 15, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 15, | |
| "data": { | |
| "text/plain": "<AxesSubplot:>" | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": "<Figure size 432x288 with 1 Axes>", | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "import statsmodels.formula.api as smf \n\nlinear_model = smf.ols('Passengers~t',data=Train).fit()\npred_linear = pd.Series(linear_model.predict(pd.DataFrame(Test['t'])))\nrmse_linear = np.sqrt(np.mean((np.array(Test['Passengers'])-np.array(pred_linear))**2))\nrmse_linear", | |
| "execution_count": 16, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 16, | |
| "data": { | |
| "text/plain": "53.199236534802715" | |
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| "metadata": {} | |
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| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "Quad = smf.ols('Passengers~t+t_squared',data=Train).fit()\npred_Quad = pd.Series(Quad.predict(Test[[\"t\",\"t_squared\"]]))\nrmse_Quad = np.sqrt(np.mean((np.array(Test['Passengers'])-np.array(pred_Quad))**2))\nrmse_Quad", | |
| "execution_count": 17, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 17, | |
| "data": { | |
| "text/plain": "48.051888979330975" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "add_sea = smf.ols('Passengers~Jan+Feb+Mar+Apr+May+Jun+Jul+Aug+Sep+Oct+Nov',data=Train).fit()\npred_add_sea = pd.Series(add_sea.predict(Test[['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov']]))\nrmse_add_sea = np.sqrt(np.mean((np.array(Test['Passengers'])-np.array(pred_add_sea))**2))\nrmse_add_sea", | |
| "execution_count": 18, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 18, | |
| "data": { | |
| "text/plain": "132.8197848142182" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "add_sea_Quad = smf.ols('Passengers~t+t_squared+Jan+Feb+Mar+Apr+May+Jun+Jul+Aug+Sep+Oct+Nov',data=Train).fit()\npred_add_sea_quad = pd.Series(add_sea_Quad.predict(Test[['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','t','t_squared']]))\nrmse_add_sea_quad = np.sqrt(np.mean((np.array(Test['Passengers'])-np.array(pred_add_sea_quad))**2))\nrmse_add_sea_quad", | |
| "execution_count": 19, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 19, | |
| "data": { | |
| "text/plain": "26.360817612086503" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "Exp = smf.ols('log_Passengers~t',data=Train).fit()\npred_Exp = pd.Series(Exp.predict(pd.DataFrame(Test['t'])))\nrmse_Exp = np.sqrt(np.mean((np.array(Test['Passengers'])-np.array(np.exp(pred_Exp)))**2))\nrmse_Exp", | |
| "execution_count": 20, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 20, | |
| "data": { | |
| "text/plain": "46.0573611031562" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "Mul_sea = smf.ols('log_Passengers~Jan+Feb+Mar+Apr+May+Jun+Jul+Aug+Sep+Oct+Nov',data = Train).fit()\npred_Mult_sea = pd.Series(Mul_sea.predict(Test))\nrmse_Mult_sea = np.sqrt(np.mean((np.array(Test['Passengers'])-np.array(np.exp(pred_Mult_sea)))**2))\nrmse_Mult_sea\n", | |
| "execution_count": 21, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 21, | |
| "data": { | |
| "text/plain": "140.06320204708638" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "Mul_Add_sea = smf.ols('log_Passengers~t+Jan+Feb+Mar+Apr+May+Jun+Jul+Aug+Sep+Oct+Nov',data = Train).fit()\npred_Mult_add_sea = pd.Series(Mul_Add_sea.predict(Test))\nrmse_Mult_add_sea = np.sqrt(np.mean((np.array(Test['Passengers'])-np.array(np.exp(pred_Mult_add_sea)))**2))\nrmse_Mult_add_sea \n", | |
| "execution_count": 22, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 22, | |
| "data": { | |
| "text/plain": "10.519172544323617" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "data = {\"MODEL\":pd.Series([\"rmse_linear\",\"rmse_Exp\",\"rmse_Quad\",\"rmse_add_sea\",\"rmse_add_sea_quad\",\"rmse_Mult_sea\",\"rmse_Mult_add_sea\"]),\"RMSE_Values\":pd.Series([rmse_linear,rmse_Exp,rmse_Quad,rmse_add_sea,rmse_add_sea_quad,rmse_Mult_sea,rmse_Mult_add_sea])}\ntable_rmse=pd.DataFrame(data)\ntable_rmse", | |
| "execution_count": 23, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 23, | |
| "data": { | |
| "text/plain": " MODEL RMSE_Values\n0 rmse_linear 53.199237\n1 rmse_Exp 46.057361\n2 rmse_Quad 48.051889\n3 rmse_add_sea 132.819785\n4 rmse_add_sea_quad 26.360818\n5 rmse_Mult_sea 140.063202\n6 rmse_Mult_add_sea 10.519173", | |
| "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>MODEL</th>\n <th>RMSE_Values</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>rmse_linear</td>\n <td>53.199237</td>\n </tr>\n <tr>\n <th>1</th>\n <td>rmse_Exp</td>\n <td>46.057361</td>\n </tr>\n <tr>\n <th>2</th>\n <td>rmse_Quad</td>\n <td>48.051889</td>\n </tr>\n <tr>\n <th>3</th>\n <td>rmse_add_sea</td>\n <td>132.819785</td>\n </tr>\n <tr>\n <th>4</th>\n <td>rmse_add_sea_quad</td>\n <td>26.360818</td>\n </tr>\n <tr>\n <th>5</th>\n <td>rmse_Mult_sea</td>\n <td>140.063202</td>\n </tr>\n <tr>\n <th>6</th>\n <td>rmse_Mult_add_sea</td>\n <td>10.519173</td>\n </tr>\n </tbody>\n</table>\n</div>" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "#WE WILL USE Multiplicative Additive Seasonality AS ITS RMSE VALUE IS THE LEAST \n#SO ITS THE BEST MODEL TO FORECAST", | |
| "execution_count": 25, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "", | |
| "execution_count": null, | |
| "outputs": [] | |
| } | |
| ], | |
| "metadata": { | |
| "gist": { | |
| "id": "", | |
| "data": { | |
| "description": "AIRLINE PASSENGERS FORECASTING.ipynb", | |
| "public": true | |
| } | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3", | |
| "language": "python" | |
| }, | |
| "language_info": { | |
| "name": "python", | |
| "version": "3.8.5", | |
| "mimetype": "text/x-python", | |
| "codemirror_mode": { | |
| "name": "ipython", | |
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| "pygments_lexer": "ipython3", | |
| "nbconvert_exporter": "python", | |
| "file_extension": ".py" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 4 | |
| } |
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