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Aditii7/Assignment Forcasting (COCO COLA ).ipynb

Created April 5, 2021 15:58
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Assignment Forcasting (COCO COLA ).ipynb
{
"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(\"CocaCola_Sales_Rawdata.xlsx\")",
"execution_count": 2,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "quarter=['Q1','Q2','Q3','Q4']\nn=data['Quarter'][0]\nn[0:2]",
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 3,
"data": {
"text/plain": "'Q1'"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "data['quarter']=0\nfor i in range(42):\n n=data['Quarter'][i]\n data['quarter'][i]=n[0:2]",
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": "<ipython-input-4-2f53b9e0d15e>:4: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n data['quarter'][i]=n[0:2]\nC:\\Users\\91966\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:670: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n iloc._setitem_with_indexer(indexer, value)\n",
"name": "stderr"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "dummy=pd.DataFrame(pd.get_dummies(data['quarter']))",
"execution_count": 5,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "dummy",
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 6,
"data": {
"text/plain": " Q1 Q2 Q3 Q4\n0 1 0 0 0\n1 0 1 0 0\n2 0 0 1 0\n3 0 0 0 1\n4 1 0 0 0\n5 0 1 0 0\n6 0 0 1 0\n7 0 0 0 1\n8 1 0 0 0\n9 0 1 0 0\n10 0 0 1 0\n11 0 0 0 1\n12 1 0 0 0\n13 0 1 0 0\n14 0 0 1 0\n15 0 0 0 1\n16 1 0 0 0\n17 0 1 0 0\n18 0 0 1 0\n19 0 0 0 1\n20 1 0 0 0\n21 0 1 0 0\n22 0 0 1 0\n23 0 0 0 1\n24 1 0 0 0\n25 0 1 0 0\n26 0 0 1 0\n27 0 0 0 1\n28 1 0 0 0\n29 0 1 0 0\n30 0 0 1 0\n31 0 0 0 1\n32 1 0 0 0\n33 0 1 0 0\n34 0 0 1 0\n35 0 0 0 1\n36 1 0 0 0\n37 0 1 0 0\n38 0 0 1 0\n39 0 0 0 1\n40 1 0 0 0\n41 0 1 0 0",
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},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df=pd.concat((data,dummy),axis=1)",
"execution_count": 7,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df",
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 8,
"data": {
"text/plain": " Quarter Sales quarter Q1 Q2 Q3 Q4\n0 Q1_86 1734.827000 Q1 1 0 0 0\n1 Q2_86 2244.960999 Q2 0 1 0 0\n2 Q3_86 2533.804993 Q3 0 0 1 0\n3 Q4_86 2154.962997 Q4 0 0 0 1\n4 Q1_87 1547.818996 Q1 1 0 0 0\n5 Q2_87 2104.411995 Q2 0 1 0 0\n6 Q3_87 2014.362999 Q3 0 0 1 0\n7 Q4_87 1991.746998 Q4 0 0 0 1\n8 Q1_88 1869.049999 Q1 1 0 0 0\n9 Q2_88 2313.631996 Q2 0 1 0 0\n10 Q3_88 2128.320000 Q3 0 0 1 0\n11 Q4_88 2026.828999 Q4 0 0 0 1\n12 Q1_89 1910.603996 Q1 1 0 0 0\n13 Q2_89 2331.164993 Q2 0 1 0 0\n14 Q3_89 2206.549995 Q3 0 0 1 0\n15 Q4_89 2173.967995 Q4 0 0 0 1\n16 Q1_90 2148.278000 Q1 1 0 0 0\n17 Q2_90 2739.307999 Q2 0 1 0 0\n18 Q3_90 2792.753998 Q3 0 0 1 0\n19 Q4_90 2556.009995 Q4 0 0 0 1\n20 Q1_91 2480.973999 Q1 1 0 0 0\n21 Q2_91 3039.522995 Q2 0 1 0 0\n22 Q3_91 3172.115997 Q3 0 0 1 0\n23 Q4_91 2879.000999 Q4 0 0 0 1\n24 Q1_92 2772.000000 Q1 1 0 0 0\n25 Q2_92 3550.000000 Q2 0 1 0 0\n26 Q3_92 3508.000000 Q3 0 0 1 0\n27 Q4_92 3243.859993 Q4 0 0 0 1\n28 Q1_93 3056.000000 Q1 1 0 0 0\n29 Q2_93 3899.000000 Q2 0 1 0 0\n30 Q3_93 3629.000000 Q3 0 0 1 0\n31 Q4_93 3373.000000 Q4 0 0 0 1\n32 Q1_94 3352.000000 Q1 1 0 0 0\n33 Q2_94 4342.000000 Q2 0 1 0 0\n34 Q3_94 4461.000000 Q3 0 0 1 0\n35 Q4_94 4017.000000 Q4 0 0 0 1\n36 Q1_95 3854.000000 Q1 1 0 0 0\n37 Q2_95 4936.000000 Q2 0 1 0 0\n38 Q3_95 4895.000000 Q3 0 0 1 0\n39 Q4_95 4333.000000 Q4 0 0 0 1\n40 Q1_96 4194.000000 Q1 1 0 0 0\n41 Q2_96 5253.000000 Q2 0 1 0 0",
"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>Quarter</th>\n <th>Sales</th>\n <th>quarter</th>\n <th>Q1</th>\n <th>Q2</th>\n <th>Q3</th>\n <th>Q4</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Q1_86</td>\n <td>1734.827000</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Q2_86</td>\n <td>2244.960999</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Q3_86</td>\n <td>2533.804993</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Q4_86</td>\n <td>2154.962997</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Q1_87</td>\n <td>1547.818996</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>Q2_87</td>\n <td>2104.411995</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>6</th>\n <td>Q3_87</td>\n <td>2014.362999</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>7</th>\n <td>Q4_87</td>\n <td>1991.746998</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>8</th>\n <td>Q1_88</td>\n <td>1869.049999</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>9</th>\n <td>Q2_88</td>\n <td>2313.631996</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>10</th>\n <td>Q3_88</td>\n <td>2128.320000</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>11</th>\n <td>Q4_88</td>\n <td>2026.828999</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>12</th>\n <td>Q1_89</td>\n <td>1910.603996</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>13</th>\n <td>Q2_89</td>\n <td>2331.164993</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>14</th>\n <td>Q3_89</td>\n <td>2206.549995</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>15</th>\n <td>Q4_89</td>\n <td>2173.967995</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>16</th>\n <td>Q1_90</td>\n <td>2148.278000</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>17</th>\n <td>Q2_90</td>\n <td>2739.307999</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>18</th>\n <td>Q3_90</td>\n <td>2792.753998</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>19</th>\n <td>Q4_90</td>\n <td>2556.009995</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>20</th>\n <td>Q1_91</td>\n <td>2480.973999</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>21</th>\n <td>Q2_91</td>\n <td>3039.522995</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>22</th>\n <td>Q3_91</td>\n <td>3172.115997</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>23</th>\n <td>Q4_91</td>\n <td>2879.000999</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>24</th>\n <td>Q1_92</td>\n <td>2772.000000</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>25</th>\n <td>Q2_92</td>\n <td>3550.000000</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>26</th>\n <td>Q3_92</td>\n <td>3508.000000</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>27</th>\n <td>Q4_92</td>\n <td>3243.859993</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>28</th>\n <td>Q1_93</td>\n <td>3056.000000</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>29</th>\n <td>Q2_93</td>\n <td>3899.000000</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>30</th>\n <td>Q3_93</td>\n <td>3629.000000</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>31</th>\n <td>Q4_93</td>\n <td>3373.000000</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>32</th>\n <td>Q1_94</td>\n <td>3352.000000</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>33</th>\n <td>Q2_94</td>\n <td>4342.000000</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>34</th>\n <td>Q3_94</td>\n <td>4461.000000</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>35</th>\n <td>Q4_94</td>\n <td>4017.000000</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>36</th>\n <td>Q1_95</td>\n <td>3854.000000</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>37</th>\n <td>Q2_95</td>\n <td>4936.000000</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>38</th>\n <td>Q3_95</td>\n <td>4895.000000</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>39</th>\n <td>Q4_95</td>\n <td>4333.000000</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>40</th>\n <td>Q1_96</td>\n <td>4194.000000</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>41</th>\n <td>Q2_96</td>\n <td>5253.000000</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df['t']=np.arange(0,42)",
"execution_count": 9,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df['t_square']=df['t']*df['t']",
"execution_count": 10,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df.head(42)",
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 11,
"data": {
"text/plain": " Quarter Sales quarter Q1 Q2 Q3 Q4 t t_square\n0 Q1_86 1734.827000 Q1 1 0 0 0 0 0\n1 Q2_86 2244.960999 Q2 0 1 0 0 1 1\n2 Q3_86 2533.804993 Q3 0 0 1 0 2 4\n3 Q4_86 2154.962997 Q4 0 0 0 1 3 9\n4 Q1_87 1547.818996 Q1 1 0 0 0 4 16\n5 Q2_87 2104.411995 Q2 0 1 0 0 5 25\n6 Q3_87 2014.362999 Q3 0 0 1 0 6 36\n7 Q4_87 1991.746998 Q4 0 0 0 1 7 49\n8 Q1_88 1869.049999 Q1 1 0 0 0 8 64\n9 Q2_88 2313.631996 Q2 0 1 0 0 9 81\n10 Q3_88 2128.320000 Q3 0 0 1 0 10 100\n11 Q4_88 2026.828999 Q4 0 0 0 1 11 121\n12 Q1_89 1910.603996 Q1 1 0 0 0 12 144\n13 Q2_89 2331.164993 Q2 0 1 0 0 13 169\n14 Q3_89 2206.549995 Q3 0 0 1 0 14 196\n15 Q4_89 2173.967995 Q4 0 0 0 1 15 225\n16 Q1_90 2148.278000 Q1 1 0 0 0 16 256\n17 Q2_90 2739.307999 Q2 0 1 0 0 17 289\n18 Q3_90 2792.753998 Q3 0 0 1 0 18 324\n19 Q4_90 2556.009995 Q4 0 0 0 1 19 361\n20 Q1_91 2480.973999 Q1 1 0 0 0 20 400\n21 Q2_91 3039.522995 Q2 0 1 0 0 21 441\n22 Q3_91 3172.115997 Q3 0 0 1 0 22 484\n23 Q4_91 2879.000999 Q4 0 0 0 1 23 529\n24 Q1_92 2772.000000 Q1 1 0 0 0 24 576\n25 Q2_92 3550.000000 Q2 0 1 0 0 25 625\n26 Q3_92 3508.000000 Q3 0 0 1 0 26 676\n27 Q4_92 3243.859993 Q4 0 0 0 1 27 729\n28 Q1_93 3056.000000 Q1 1 0 0 0 28 784\n29 Q2_93 3899.000000 Q2 0 1 0 0 29 841\n30 Q3_93 3629.000000 Q3 0 0 1 0 30 900\n31 Q4_93 3373.000000 Q4 0 0 0 1 31 961\n32 Q1_94 3352.000000 Q1 1 0 0 0 32 1024\n33 Q2_94 4342.000000 Q2 0 1 0 0 33 1089\n34 Q3_94 4461.000000 Q3 0 0 1 0 34 1156\n35 Q4_94 4017.000000 Q4 0 0 0 1 35 1225\n36 Q1_95 3854.000000 Q1 1 0 0 0 36 1296\n37 Q2_95 4936.000000 Q2 0 1 0 0 37 1369\n38 Q3_95 4895.000000 Q3 0 0 1 0 38 1444\n39 Q4_95 4333.000000 Q4 0 0 0 1 39 1521\n40 Q1_96 4194.000000 Q1 1 0 0 0 40 1600\n41 Q2_96 5253.000000 Q2 0 1 0 0 41 1681",
"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>Quarter</th>\n <th>Sales</th>\n <th>quarter</th>\n <th>Q1</th>\n <th>Q2</th>\n <th>Q3</th>\n <th>Q4</th>\n <th>t</th>\n <th>t_square</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Q1_86</td>\n <td>1734.827000</td>\n <td>Q1</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 </tr>\n <tr>\n <th>1</th>\n <td>Q2_86</td>\n <td>2244.960999</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Q3_86</td>\n <td>2533.804993</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>2</td>\n <td>4</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Q4_86</td>\n <td>2154.962997</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>3</td>\n <td>9</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Q1_87</td>\n <td>1547.818996</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>4</td>\n <td>16</td>\n </tr>\n <tr>\n <th>5</th>\n <td>Q2_87</td>\n <td>2104.411995</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>5</td>\n <td>25</td>\n </tr>\n <tr>\n <th>6</th>\n <td>Q3_87</td>\n <td>2014.362999</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>6</td>\n <td>36</td>\n </tr>\n <tr>\n <th>7</th>\n <td>Q4_87</td>\n <td>1991.746998</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>7</td>\n <td>49</td>\n </tr>\n <tr>\n <th>8</th>\n <td>Q1_88</td>\n <td>1869.049999</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>8</td>\n <td>64</td>\n </tr>\n <tr>\n <th>9</th>\n <td>Q2_88</td>\n <td>2313.631996</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>9</td>\n <td>81</td>\n </tr>\n <tr>\n <th>10</th>\n <td>Q3_88</td>\n <td>2128.320000</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>10</td>\n <td>100</td>\n </tr>\n <tr>\n <th>11</th>\n <td>Q4_88</td>\n <td>2026.828999</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>11</td>\n <td>121</td>\n </tr>\n <tr>\n <th>12</th>\n <td>Q1_89</td>\n <td>1910.603996</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>12</td>\n <td>144</td>\n </tr>\n <tr>\n <th>13</th>\n <td>Q2_89</td>\n <td>2331.164993</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>13</td>\n <td>169</td>\n </tr>\n <tr>\n <th>14</th>\n <td>Q3_89</td>\n <td>2206.549995</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>14</td>\n <td>196</td>\n </tr>\n <tr>\n <th>15</th>\n <td>Q4_89</td>\n <td>2173.967995</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>15</td>\n <td>225</td>\n </tr>\n <tr>\n <th>16</th>\n <td>Q1_90</td>\n <td>2148.278000</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>16</td>\n <td>256</td>\n </tr>\n <tr>\n <th>17</th>\n <td>Q2_90</td>\n <td>2739.307999</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>17</td>\n <td>289</td>\n </tr>\n <tr>\n <th>18</th>\n <td>Q3_90</td>\n <td>2792.753998</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>18</td>\n <td>324</td>\n </tr>\n <tr>\n <th>19</th>\n <td>Q4_90</td>\n <td>2556.009995</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>19</td>\n <td>361</td>\n </tr>\n <tr>\n <th>20</th>\n <td>Q1_91</td>\n <td>2480.973999</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>20</td>\n <td>400</td>\n </tr>\n <tr>\n <th>21</th>\n <td>Q2_91</td>\n <td>3039.522995</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>21</td>\n <td>441</td>\n </tr>\n <tr>\n <th>22</th>\n <td>Q3_91</td>\n <td>3172.115997</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>22</td>\n <td>484</td>\n </tr>\n <tr>\n <th>23</th>\n <td>Q4_91</td>\n <td>2879.000999</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>23</td>\n <td>529</td>\n </tr>\n <tr>\n <th>24</th>\n <td>Q1_92</td>\n <td>2772.000000</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>24</td>\n <td>576</td>\n </tr>\n <tr>\n <th>25</th>\n <td>Q2_92</td>\n <td>3550.000000</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>25</td>\n <td>625</td>\n </tr>\n <tr>\n <th>26</th>\n <td>Q3_92</td>\n <td>3508.000000</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>26</td>\n <td>676</td>\n </tr>\n <tr>\n <th>27</th>\n <td>Q4_92</td>\n <td>3243.859993</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>27</td>\n <td>729</td>\n </tr>\n <tr>\n <th>28</th>\n <td>Q1_93</td>\n <td>3056.000000</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>28</td>\n <td>784</td>\n </tr>\n <tr>\n <th>29</th>\n <td>Q2_93</td>\n <td>3899.000000</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>29</td>\n <td>841</td>\n </tr>\n <tr>\n <th>30</th>\n <td>Q3_93</td>\n <td>3629.000000</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>30</td>\n <td>900</td>\n </tr>\n <tr>\n <th>31</th>\n <td>Q4_93</td>\n <td>3373.000000</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>31</td>\n <td>961</td>\n </tr>\n <tr>\n <th>32</th>\n <td>Q1_94</td>\n <td>3352.000000</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>32</td>\n <td>1024</td>\n </tr>\n <tr>\n <th>33</th>\n <td>Q2_94</td>\n <td>4342.000000</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>33</td>\n <td>1089</td>\n </tr>\n <tr>\n <th>34</th>\n <td>Q3_94</td>\n <td>4461.000000</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>34</td>\n <td>1156</td>\n </tr>\n <tr>\n <th>35</th>\n <td>Q4_94</td>\n <td>4017.000000</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>35</td>\n <td>1225</td>\n </tr>\n <tr>\n <th>36</th>\n <td>Q1_95</td>\n <td>3854.000000</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>36</td>\n <td>1296</td>\n </tr>\n <tr>\n <th>37</th>\n <td>Q2_95</td>\n <td>4936.000000</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>37</td>\n <td>1369</td>\n </tr>\n <tr>\n <th>38</th>\n <td>Q3_95</td>\n <td>4895.000000</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>38</td>\n <td>1444</td>\n </tr>\n <tr>\n <th>39</th>\n <td>Q4_95</td>\n <td>4333.000000</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>39</td>\n <td>1521</td>\n </tr>\n <tr>\n <th>40</th>\n <td>Q1_96</td>\n <td>4194.000000</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>40</td>\n <td>1600</td>\n </tr>\n <tr>\n <th>41</th>\n <td>Q2_96</td>\n <td>5253.000000</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>41</td>\n <td>1681</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "log_sales=np.log(df['Sales'])",
"execution_count": 12,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df['log_Sales']=log_sales",
"execution_count": 13,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df",
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 14,
"data": {
"text/plain": " Quarter Sales quarter Q1 Q2 Q3 Q4 t t_square log_Sales\n0 Q1_86 1734.827000 Q1 1 0 0 0 0 0 7.458663\n1 Q2_86 2244.960999 Q2 0 1 0 0 1 1 7.716443\n2 Q3_86 2533.804993 Q3 0 0 1 0 2 4 7.837477\n3 Q4_86 2154.962997 Q4 0 0 0 1 3 9 7.675529\n4 Q1_87 1547.818996 Q1 1 0 0 0 4 16 7.344602\n5 Q2_87 2104.411995 Q2 0 1 0 0 5 25 7.651791\n6 Q3_87 2014.362999 Q3 0 0 1 0 6 36 7.608058\n7 Q4_87 1991.746998 Q4 0 0 0 1 7 49 7.596767\n8 Q1_88 1869.049999 Q1 1 0 0 0 8 64 7.533186\n9 Q2_88 2313.631996 Q2 0 1 0 0 9 81 7.746574\n10 Q3_88 2128.320000 Q3 0 0 1 0 10 100 7.663088\n11 Q4_88 2026.828999 Q4 0 0 0 1 11 121 7.614228\n12 Q1_89 1910.603996 Q1 1 0 0 0 12 144 7.555175\n13 Q2_89 2331.164993 Q2 0 1 0 0 13 169 7.754123\n14 Q3_89 2206.549995 Q3 0 0 1 0 14 196 7.699185\n15 Q4_89 2173.967995 Q4 0 0 0 1 15 225 7.684309\n16 Q1_90 2148.278000 Q1 1 0 0 0 16 256 7.672422\n17 Q2_90 2739.307999 Q2 0 1 0 0 17 289 7.915461\n18 Q3_90 2792.753998 Q3 0 0 1 0 18 324 7.934783\n19 Q4_90 2556.009995 Q4 0 0 0 1 19 361 7.846203\n20 Q1_91 2480.973999 Q1 1 0 0 0 20 400 7.816407\n21 Q2_91 3039.522995 Q2 0 1 0 0 21 441 8.019456\n22 Q3_91 3172.115997 Q3 0 0 1 0 22 484 8.062154\n23 Q4_91 2879.000999 Q4 0 0 0 1 23 529 7.965199\n24 Q1_92 2772.000000 Q1 1 0 0 0 24 576 7.927324\n25 Q2_92 3550.000000 Q2 0 1 0 0 25 625 8.174703\n26 Q3_92 3508.000000 Q3 0 0 1 0 26 676 8.162801\n27 Q4_92 3243.859993 Q4 0 0 0 1 27 729 8.084519\n28 Q1_93 3056.000000 Q1 1 0 0 0 28 784 8.024862\n29 Q2_93 3899.000000 Q2 0 1 0 0 29 841 8.268475\n30 Q3_93 3629.000000 Q3 0 0 1 0 30 900 8.196712\n31 Q4_93 3373.000000 Q4 0 0 0 1 31 961 8.123558\n32 Q1_94 3352.000000 Q1 1 0 0 0 32 1024 8.117312\n33 Q2_94 4342.000000 Q2 0 1 0 0 33 1089 8.376090\n34 Q3_94 4461.000000 Q3 0 0 1 0 34 1156 8.403128\n35 Q4_94 4017.000000 Q4 0 0 0 1 35 1225 8.298291\n36 Q1_95 3854.000000 Q1 1 0 0 0 36 1296 8.256867\n37 Q2_95 4936.000000 Q2 0 1 0 0 37 1369 8.504311\n38 Q3_95 4895.000000 Q3 0 0 1 0 38 1444 8.495970\n39 Q4_95 4333.000000 Q4 0 0 0 1 39 1521 8.374015\n40 Q1_96 4194.000000 Q1 1 0 0 0 40 1600 8.341410\n41 Q2_96 5253.000000 Q2 0 1 0 0 41 1681 8.566555",
"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>Quarter</th>\n <th>Sales</th>\n <th>quarter</th>\n <th>Q1</th>\n <th>Q2</th>\n <th>Q3</th>\n <th>Q4</th>\n <th>t</th>\n <th>t_square</th>\n <th>log_Sales</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Q1_86</td>\n <td>1734.827000</td>\n <td>Q1</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>7.458663</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Q2_86</td>\n <td>2244.960999</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>7.716443</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Q3_86</td>\n <td>2533.804993</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>2</td>\n <td>4</td>\n <td>7.837477</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Q4_86</td>\n <td>2154.962997</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>3</td>\n <td>9</td>\n <td>7.675529</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Q1_87</td>\n <td>1547.818996</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>4</td>\n <td>16</td>\n <td>7.344602</td>\n </tr>\n <tr>\n <th>5</th>\n <td>Q2_87</td>\n <td>2104.411995</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>5</td>\n <td>25</td>\n <td>7.651791</td>\n </tr>\n <tr>\n <th>6</th>\n <td>Q3_87</td>\n <td>2014.362999</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>6</td>\n <td>36</td>\n <td>7.608058</td>\n </tr>\n <tr>\n <th>7</th>\n <td>Q4_87</td>\n <td>1991.746998</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>7</td>\n <td>49</td>\n <td>7.596767</td>\n </tr>\n <tr>\n <th>8</th>\n <td>Q1_88</td>\n <td>1869.049999</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>8</td>\n <td>64</td>\n <td>7.533186</td>\n </tr>\n <tr>\n <th>9</th>\n <td>Q2_88</td>\n <td>2313.631996</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>9</td>\n <td>81</td>\n <td>7.746574</td>\n </tr>\n <tr>\n <th>10</th>\n <td>Q3_88</td>\n <td>2128.320000</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>10</td>\n <td>100</td>\n <td>7.663088</td>\n </tr>\n <tr>\n <th>11</th>\n <td>Q4_88</td>\n <td>2026.828999</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>11</td>\n <td>121</td>\n <td>7.614228</td>\n </tr>\n <tr>\n <th>12</th>\n <td>Q1_89</td>\n <td>1910.603996</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>12</td>\n <td>144</td>\n <td>7.555175</td>\n </tr>\n <tr>\n <th>13</th>\n <td>Q2_89</td>\n <td>2331.164993</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>13</td>\n <td>169</td>\n <td>7.754123</td>\n </tr>\n <tr>\n <th>14</th>\n <td>Q3_89</td>\n <td>2206.549995</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>14</td>\n <td>196</td>\n <td>7.699185</td>\n </tr>\n <tr>\n <th>15</th>\n <td>Q4_89</td>\n <td>2173.967995</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>15</td>\n <td>225</td>\n <td>7.684309</td>\n </tr>\n <tr>\n <th>16</th>\n <td>Q1_90</td>\n <td>2148.278000</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>16</td>\n <td>256</td>\n <td>7.672422</td>\n </tr>\n <tr>\n <th>17</th>\n <td>Q2_90</td>\n <td>2739.307999</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>17</td>\n <td>289</td>\n <td>7.915461</td>\n </tr>\n <tr>\n <th>18</th>\n <td>Q3_90</td>\n <td>2792.753998</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>18</td>\n <td>324</td>\n <td>7.934783</td>\n </tr>\n <tr>\n <th>19</th>\n <td>Q4_90</td>\n <td>2556.009995</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>19</td>\n <td>361</td>\n <td>7.846203</td>\n </tr>\n <tr>\n <th>20</th>\n <td>Q1_91</td>\n <td>2480.973999</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>20</td>\n <td>400</td>\n <td>7.816407</td>\n </tr>\n <tr>\n <th>21</th>\n <td>Q2_91</td>\n <td>3039.522995</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>21</td>\n <td>441</td>\n <td>8.019456</td>\n </tr>\n <tr>\n <th>22</th>\n <td>Q3_91</td>\n <td>3172.115997</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>22</td>\n <td>484</td>\n <td>8.062154</td>\n </tr>\n <tr>\n <th>23</th>\n <td>Q4_91</td>\n <td>2879.000999</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>23</td>\n <td>529</td>\n <td>7.965199</td>\n </tr>\n <tr>\n <th>24</th>\n <td>Q1_92</td>\n <td>2772.000000</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>24</td>\n <td>576</td>\n <td>7.927324</td>\n </tr>\n <tr>\n <th>25</th>\n <td>Q2_92</td>\n <td>3550.000000</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>25</td>\n <td>625</td>\n <td>8.174703</td>\n </tr>\n <tr>\n <th>26</th>\n <td>Q3_92</td>\n <td>3508.000000</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>26</td>\n <td>676</td>\n <td>8.162801</td>\n </tr>\n <tr>\n <th>27</th>\n <td>Q4_92</td>\n <td>3243.859993</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>27</td>\n <td>729</td>\n <td>8.084519</td>\n </tr>\n <tr>\n <th>28</th>\n <td>Q1_93</td>\n <td>3056.000000</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>28</td>\n <td>784</td>\n <td>8.024862</td>\n </tr>\n <tr>\n <th>29</th>\n <td>Q2_93</td>\n <td>3899.000000</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>29</td>\n <td>841</td>\n <td>8.268475</td>\n </tr>\n <tr>\n <th>30</th>\n <td>Q3_93</td>\n <td>3629.000000</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>30</td>\n <td>900</td>\n <td>8.196712</td>\n </tr>\n <tr>\n <th>31</th>\n <td>Q4_93</td>\n <td>3373.000000</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>31</td>\n <td>961</td>\n <td>8.123558</td>\n </tr>\n <tr>\n <th>32</th>\n <td>Q1_94</td>\n <td>3352.000000</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>32</td>\n <td>1024</td>\n <td>8.117312</td>\n </tr>\n <tr>\n <th>33</th>\n <td>Q2_94</td>\n <td>4342.000000</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>33</td>\n <td>1089</td>\n <td>8.376090</td>\n </tr>\n <tr>\n <th>34</th>\n <td>Q3_94</td>\n <td>4461.000000</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>34</td>\n <td>1156</td>\n <td>8.403128</td>\n </tr>\n <tr>\n <th>35</th>\n <td>Q4_94</td>\n <td>4017.000000</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>35</td>\n <td>1225</td>\n <td>8.298291</td>\n </tr>\n <tr>\n <th>36</th>\n <td>Q1_95</td>\n <td>3854.000000</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>36</td>\n <td>1296</td>\n <td>8.256867</td>\n </tr>\n <tr>\n <th>37</th>\n <td>Q2_95</td>\n <td>4936.000000</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>37</td>\n <td>1369</td>\n <td>8.504311</td>\n </tr>\n <tr>\n <th>38</th>\n <td>Q3_95</td>\n <td>4895.000000</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>38</td>\n <td>1444</td>\n <td>8.495970</td>\n </tr>\n <tr>\n <th>39</th>\n <td>Q4_95</td>\n <td>4333.000000</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>39</td>\n <td>1521</td>\n <td>8.374015</td>\n </tr>\n <tr>\n <th>40</th>\n <td>Q1_96</td>\n <td>4194.000000</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>40</td>\n <td>1600</td>\n <td>8.341410</td>\n </tr>\n <tr>\n <th>41</th>\n <td>Q2_96</td>\n <td>5253.000000</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>41</td>\n <td>1681</td>\n <td>8.566555</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "train=df.head(37)\ntest=df.tail(4)",
"execution_count": 15,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "train",
"execution_count": 16,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 16,
"data": {
"text/plain": " Quarter Sales quarter Q1 Q2 Q3 Q4 t t_square log_Sales\n0 Q1_86 1734.827000 Q1 1 0 0 0 0 0 7.458663\n1 Q2_86 2244.960999 Q2 0 1 0 0 1 1 7.716443\n2 Q3_86 2533.804993 Q3 0 0 1 0 2 4 7.837477\n3 Q4_86 2154.962997 Q4 0 0 0 1 3 9 7.675529\n4 Q1_87 1547.818996 Q1 1 0 0 0 4 16 7.344602\n5 Q2_87 2104.411995 Q2 0 1 0 0 5 25 7.651791\n6 Q3_87 2014.362999 Q3 0 0 1 0 6 36 7.608058\n7 Q4_87 1991.746998 Q4 0 0 0 1 7 49 7.596767\n8 Q1_88 1869.049999 Q1 1 0 0 0 8 64 7.533186\n9 Q2_88 2313.631996 Q2 0 1 0 0 9 81 7.746574\n10 Q3_88 2128.320000 Q3 0 0 1 0 10 100 7.663088\n11 Q4_88 2026.828999 Q4 0 0 0 1 11 121 7.614228\n12 Q1_89 1910.603996 Q1 1 0 0 0 12 144 7.555175\n13 Q2_89 2331.164993 Q2 0 1 0 0 13 169 7.754123\n14 Q3_89 2206.549995 Q3 0 0 1 0 14 196 7.699185\n15 Q4_89 2173.967995 Q4 0 0 0 1 15 225 7.684309\n16 Q1_90 2148.278000 Q1 1 0 0 0 16 256 7.672422\n17 Q2_90 2739.307999 Q2 0 1 0 0 17 289 7.915461\n18 Q3_90 2792.753998 Q3 0 0 1 0 18 324 7.934783\n19 Q4_90 2556.009995 Q4 0 0 0 1 19 361 7.846203\n20 Q1_91 2480.973999 Q1 1 0 0 0 20 400 7.816407\n21 Q2_91 3039.522995 Q2 0 1 0 0 21 441 8.019456\n22 Q3_91 3172.115997 Q3 0 0 1 0 22 484 8.062154\n23 Q4_91 2879.000999 Q4 0 0 0 1 23 529 7.965199\n24 Q1_92 2772.000000 Q1 1 0 0 0 24 576 7.927324\n25 Q2_92 3550.000000 Q2 0 1 0 0 25 625 8.174703\n26 Q3_92 3508.000000 Q3 0 0 1 0 26 676 8.162801\n27 Q4_92 3243.859993 Q4 0 0 0 1 27 729 8.084519\n28 Q1_93 3056.000000 Q1 1 0 0 0 28 784 8.024862\n29 Q2_93 3899.000000 Q2 0 1 0 0 29 841 8.268475\n30 Q3_93 3629.000000 Q3 0 0 1 0 30 900 8.196712\n31 Q4_93 3373.000000 Q4 0 0 0 1 31 961 8.123558\n32 Q1_94 3352.000000 Q1 1 0 0 0 32 1024 8.117312\n33 Q2_94 4342.000000 Q2 0 1 0 0 33 1089 8.376090\n34 Q3_94 4461.000000 Q3 0 0 1 0 34 1156 8.403128\n35 Q4_94 4017.000000 Q4 0 0 0 1 35 1225 8.298291\n36 Q1_95 3854.000000 Q1 1 0 0 0 36 1296 8.256867",
"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>Quarter</th>\n <th>Sales</th>\n <th>quarter</th>\n <th>Q1</th>\n <th>Q2</th>\n <th>Q3</th>\n <th>Q4</th>\n <th>t</th>\n <th>t_square</th>\n <th>log_Sales</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Q1_86</td>\n <td>1734.827000</td>\n <td>Q1</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>7.458663</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Q2_86</td>\n <td>2244.960999</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>7.716443</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Q3_86</td>\n <td>2533.804993</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>2</td>\n <td>4</td>\n <td>7.837477</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Q4_86</td>\n <td>2154.962997</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>3</td>\n <td>9</td>\n <td>7.675529</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Q1_87</td>\n <td>1547.818996</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>4</td>\n <td>16</td>\n <td>7.344602</td>\n </tr>\n <tr>\n <th>5</th>\n <td>Q2_87</td>\n <td>2104.411995</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>5</td>\n <td>25</td>\n <td>7.651791</td>\n </tr>\n <tr>\n <th>6</th>\n <td>Q3_87</td>\n <td>2014.362999</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>6</td>\n <td>36</td>\n <td>7.608058</td>\n </tr>\n <tr>\n <th>7</th>\n <td>Q4_87</td>\n <td>1991.746998</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>7</td>\n <td>49</td>\n <td>7.596767</td>\n </tr>\n <tr>\n <th>8</th>\n <td>Q1_88</td>\n <td>1869.049999</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>8</td>\n <td>64</td>\n <td>7.533186</td>\n </tr>\n <tr>\n <th>9</th>\n <td>Q2_88</td>\n <td>2313.631996</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>9</td>\n <td>81</td>\n <td>7.746574</td>\n </tr>\n <tr>\n <th>10</th>\n <td>Q3_88</td>\n <td>2128.320000</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>10</td>\n <td>100</td>\n <td>7.663088</td>\n </tr>\n <tr>\n <th>11</th>\n <td>Q4_88</td>\n <td>2026.828999</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>11</td>\n <td>121</td>\n <td>7.614228</td>\n </tr>\n <tr>\n <th>12</th>\n <td>Q1_89</td>\n <td>1910.603996</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>12</td>\n <td>144</td>\n <td>7.555175</td>\n </tr>\n <tr>\n <th>13</th>\n <td>Q2_89</td>\n <td>2331.164993</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>13</td>\n <td>169</td>\n <td>7.754123</td>\n </tr>\n <tr>\n <th>14</th>\n <td>Q3_89</td>\n <td>2206.549995</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>14</td>\n <td>196</td>\n <td>7.699185</td>\n </tr>\n <tr>\n <th>15</th>\n <td>Q4_89</td>\n <td>2173.967995</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>15</td>\n <td>225</td>\n <td>7.684309</td>\n </tr>\n <tr>\n <th>16</th>\n <td>Q1_90</td>\n <td>2148.278000</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>16</td>\n <td>256</td>\n <td>7.672422</td>\n </tr>\n <tr>\n <th>17</th>\n <td>Q2_90</td>\n <td>2739.307999</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>17</td>\n <td>289</td>\n <td>7.915461</td>\n </tr>\n <tr>\n <th>18</th>\n <td>Q3_90</td>\n <td>2792.753998</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>18</td>\n <td>324</td>\n <td>7.934783</td>\n </tr>\n <tr>\n <th>19</th>\n <td>Q4_90</td>\n <td>2556.009995</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>19</td>\n <td>361</td>\n <td>7.846203</td>\n </tr>\n <tr>\n <th>20</th>\n <td>Q1_91</td>\n <td>2480.973999</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>20</td>\n <td>400</td>\n <td>7.816407</td>\n </tr>\n <tr>\n <th>21</th>\n <td>Q2_91</td>\n <td>3039.522995</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>21</td>\n <td>441</td>\n <td>8.019456</td>\n </tr>\n <tr>\n <th>22</th>\n <td>Q3_91</td>\n <td>3172.115997</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>22</td>\n <td>484</td>\n <td>8.062154</td>\n </tr>\n <tr>\n <th>23</th>\n <td>Q4_91</td>\n <td>2879.000999</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>23</td>\n <td>529</td>\n <td>7.965199</td>\n </tr>\n <tr>\n <th>24</th>\n <td>Q1_92</td>\n <td>2772.000000</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>24</td>\n <td>576</td>\n <td>7.927324</td>\n </tr>\n <tr>\n <th>25</th>\n <td>Q2_92</td>\n <td>3550.000000</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>25</td>\n <td>625</td>\n <td>8.174703</td>\n </tr>\n <tr>\n <th>26</th>\n <td>Q3_92</td>\n <td>3508.000000</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>26</td>\n <td>676</td>\n <td>8.162801</td>\n </tr>\n <tr>\n <th>27</th>\n <td>Q4_92</td>\n <td>3243.859993</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>27</td>\n <td>729</td>\n <td>8.084519</td>\n </tr>\n <tr>\n <th>28</th>\n <td>Q1_93</td>\n <td>3056.000000</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>28</td>\n <td>784</td>\n <td>8.024862</td>\n </tr>\n <tr>\n <th>29</th>\n <td>Q2_93</td>\n <td>3899.000000</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>29</td>\n <td>841</td>\n <td>8.268475</td>\n </tr>\n <tr>\n <th>30</th>\n <td>Q3_93</td>\n <td>3629.000000</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>30</td>\n <td>900</td>\n <td>8.196712</td>\n </tr>\n <tr>\n <th>31</th>\n <td>Q4_93</td>\n <td>3373.000000</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>31</td>\n <td>961</td>\n <td>8.123558</td>\n </tr>\n <tr>\n <th>32</th>\n <td>Q1_94</td>\n <td>3352.000000</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>32</td>\n <td>1024</td>\n <td>8.117312</td>\n </tr>\n <tr>\n <th>33</th>\n <td>Q2_94</td>\n <td>4342.000000</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>33</td>\n <td>1089</td>\n <td>8.376090</td>\n </tr>\n <tr>\n <th>34</th>\n <td>Q3_94</td>\n <td>4461.000000</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>34</td>\n <td>1156</td>\n <td>8.403128</td>\n </tr>\n <tr>\n <th>35</th>\n <td>Q4_94</td>\n <td>4017.000000</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>35</td>\n <td>1225</td>\n <td>8.298291</td>\n </tr>\n <tr>\n <th>36</th>\n <td>Q1_95</td>\n <td>3854.000000</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>36</td>\n <td>1296</td>\n <td>8.256867</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "test",
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 17,
"data": {
"text/plain": " Quarter Sales quarter Q1 Q2 Q3 Q4 t t_square log_Sales\n38 Q3_95 4895.0 Q3 0 0 1 0 38 1444 8.495970\n39 Q4_95 4333.0 Q4 0 0 0 1 39 1521 8.374015\n40 Q1_96 4194.0 Q1 1 0 0 0 40 1600 8.341410\n41 Q2_96 5253.0 Q2 0 1 0 0 41 1681 8.566555",
"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>Quarter</th>\n <th>Sales</th>\n <th>quarter</th>\n <th>Q1</th>\n <th>Q2</th>\n <th>Q3</th>\n <th>Q4</th>\n <th>t</th>\n <th>t_square</th>\n <th>log_Sales</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>38</th>\n <td>Q3_95</td>\n <td>4895.0</td>\n <td>Q3</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>38</td>\n <td>1444</td>\n <td>8.495970</td>\n </tr>\n <tr>\n <th>39</th>\n <td>Q4_95</td>\n <td>4333.0</td>\n <td>Q4</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>39</td>\n <td>1521</td>\n <td>8.374015</td>\n </tr>\n <tr>\n <th>40</th>\n <td>Q1_96</td>\n <td>4194.0</td>\n <td>Q1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>40</td>\n <td>1600</td>\n <td>8.341410</td>\n </tr>\n <tr>\n <th>41</th>\n <td>Q2_96</td>\n <td>5253.0</td>\n <td>Q2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>41</td>\n <td>1681</td>\n <td>8.566555</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df.Sales.plot()",
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 18,
"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 = smf.ols('Sales~t',data=train).fit()\npred_linear = pd.Series(linear.predict(pd.DataFrame(test['t'])))\nrmse_linear = np.sqrt(np.mean((np.array(test['Sales'])-np.array(pred_linear))**2))\nrmse_linear\n",
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 19,
"data": {
"text/plain": "671.6427504390446"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "Quad = smf.ols('Sales~t+t_square',data=train).fit()\npred_Quad = pd.Series(Quad.predict(test[[\"t\",\"t_square\"]]))\nrmse_Quad = np.sqrt(np.mean((np.array(test['Sales'])-np.array(pred_Quad))**2))\nrmse_Quad",
"execution_count": 20,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 20,
"data": {
"text/plain": "424.24008962792504"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "Exp = smf.ols('log_Sales~t',data=train).fit()\npred_Exp = pd.Series(Exp.predict(pd.DataFrame(test['t'])))\nrmse_Exp = np.sqrt(np.mean((np.array(test['Sales'])-np.array(np.exp(pred_Exp)))**2))\nrmse_Exp",
"execution_count": 21,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 21,
"data": {
"text/plain": "513.4107967981239"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "add_sea = smf.ols('Sales~Q1+Q2+Q3+Q4',data=train).fit()\npred_add_sea = pd.Series(add_sea.predict(test[['Q1','Q2','Q3','Q4']]))\nrmse_add_sea = np.sqrt(np.mean((np.array(test['Sales'])-np.array(pred_add_sea))**2))\nrmse_add_sea",
"execution_count": 22,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 22,
"data": {
"text/plain": "1917.862861259638"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "add_sea_Quad = smf.ols('Sales~t+t_square+Q1+Q2+Q3+Q4',data=train).fit()\npred_add_sea_quad = pd.Series(add_sea_Quad.predict(test[['Q1','Q2','Q3','Q4','t','t_square']]))\nrmse_add_sea_quad = np.sqrt(np.mean((np.array(test['Sales'])-np.array(pred_add_sea_quad))**2))\nrmse_add_sea_quad ",
"execution_count": 23,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 23,
"data": {
"text/plain": "265.82796918585746"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "Mul_sea = smf.ols('log_Sales~Q1+Q2+Q3+Q4',data = train).fit()\npred_Mult_sea = pd.Series(Mul_sea.predict(test))\nrmse_Mult_sea = np.sqrt(np.mean((np.array(test['Sales'])-np.array(np.exp(pred_Mult_sea)))**2))\nrmse_Mult_sea\n",
"execution_count": 24,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 24,
"data": {
"text/plain": "2010.1194325040858"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "Mul_Add_sea = smf.ols('log_Sales~t+Q1+Q2+Q3+Q4',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['Sales'])-np.array(np.exp(pred_Mult_add_sea)))**2))\nrmse_Mult_add_sea \n",
"execution_count": 25,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 25,
"data": {
"text/plain": "262.4958466946943"
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"metadata": {}
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"metadata": {
"trusted": true
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"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])}",
"execution_count": 26,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "data",
"execution_count": 27,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 27,
"data": {
"text/plain": "{'MODEL': 0 rmse_linear\n 1 rmse_Exp\n 2 rmse_Quad\n 3 rmse_add_sea\n 4 rmse_add_sea_quad\n 5 rmse_Mult_sea\n 6 rmse_Mult_add_sea\n dtype: object,\n 'RMSE_Values': 0 671.642750\n 1 513.410797\n 2 424.240090\n 3 1917.862861\n 4 265.827969\n 5 2010.119433\n 6 262.495847\n dtype: float64}"
},
"metadata": {}
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{
"metadata": {
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"cell_type": "code",
"source": "table_rmse=pd.DataFrame(data)\ntable_rmse",
"execution_count": 28,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 28,
"data": {
"text/plain": " MODEL RMSE_Values\n0 rmse_linear 671.642750\n1 rmse_Exp 513.410797\n2 rmse_Quad 424.240090\n3 rmse_add_sea 1917.862861\n4 rmse_add_sea_quad 265.827969\n5 rmse_Mult_sea 2010.119433\n6 rmse_Mult_add_sea 262.495847",
"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>671.642750</td>\n </tr>\n <tr>\n <th>1</th>\n <td>rmse_Exp</td>\n <td>513.410797</td>\n </tr>\n <tr>\n <th>2</th>\n <td>rmse_Quad</td>\n <td>424.240090</td>\n </tr>\n <tr>\n <th>3</th>\n <td>rmse_add_sea</td>\n <td>1917.862861</td>\n </tr>\n <tr>\n <th>4</th>\n <td>rmse_add_sea_quad</td>\n <td>265.827969</td>\n </tr>\n <tr>\n <th>5</th>\n <td>rmse_Mult_sea</td>\n <td>2010.119433</td>\n </tr>\n <tr>\n <th>6</th>\n <td>rmse_Mult_add_sea</td>\n <td>262.495847</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": 29,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
},
{
"metadata": {
"trusted": true
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"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
}
],
"metadata": {
"gist": {
"id": "",
"data": {
"description": " forecast (coca cola).ipynb",
"public": true
}
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3",
"language": "python"
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"file_extension": ".py"
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