Time_series_ML_AK.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('Alcohol_Sales.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>S4248SM144NCEN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1992-01-01</td>\n",
       "      <td>3459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1992-02-01</td>\n",
       "      <td>3458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1992-03-01</td>\n",
       "      <td>4002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1992-04-01</td>\n",
       "      <td>4564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1992-05-01</td>\n",
       "      <td>4221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>2018-09-01</td>\n",
       "      <td>12396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>13914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>2018-11-01</td>\n",
       "      <td>14174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>15504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>10718</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>325 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           DATE  S4248SM144NCEN\n",
       "0    1992-01-01            3459\n",
       "1    1992-02-01            3458\n",
       "2    1992-03-01            4002\n",
       "3    1992-04-01            4564\n",
       "4    1992-05-01            4221\n",
       "..          ...             ...\n",
       "320  2018-09-01           12396\n",
       "321  2018-10-01           13914\n",
       "322  2018-11-01           14174\n",
       "323  2018-12-01           15504\n",
       "324  2019-01-01           10718\n",
       "\n",
       "[325 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "rename the columns of the df dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns=['Date','Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Date',axis=1,inplace=True)           #  we are droping gate column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['last_mon']=df['Sales'].shift(1)\n",
    "df['2last_mon']=df['Sales'].shift(2)      # since our sales data depends on previous month sales, therefore we created 3 columns having those last month sales data.\n",
    "df['3last_mon']=df['Sales'].shift(3)\n",
    "df.dropna(inplace=True)                   # dropping the null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "      <th>last_mon</th>\n",
       "      <th>2last_mon</th>\n",
       "      <th>3last_mon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4564</td>\n",
       "      <td>4002.0</td>\n",
       "      <td>3458.0</td>\n",
       "      <td>3459.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4221</td>\n",
       "      <td>4564.0</td>\n",
       "      <td>4002.0</td>\n",
       "      <td>3458.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4529</td>\n",
       "      <td>4221.0</td>\n",
       "      <td>4564.0</td>\n",
       "      <td>4002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4466</td>\n",
       "      <td>4529.0</td>\n",
       "      <td>4221.0</td>\n",
       "      <td>4564.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4137</td>\n",
       "      <td>4466.0</td>\n",
       "      <td>4529.0</td>\n",
       "      <td>4221.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>12396</td>\n",
       "      <td>14257.0</td>\n",
       "      <td>12640.0</td>\n",
       "      <td>14583.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>13914</td>\n",
       "      <td>12396.0</td>\n",
       "      <td>14257.0</td>\n",
       "      <td>12640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>14174</td>\n",
       "      <td>13914.0</td>\n",
       "      <td>12396.0</td>\n",
       "      <td>14257.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>15504</td>\n",
       "      <td>14174.0</td>\n",
       "      <td>13914.0</td>\n",
       "      <td>12396.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>10718</td>\n",
       "      <td>15504.0</td>\n",
       "      <td>14174.0</td>\n",
       "      <td>13914.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>322 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sales  last_mon  2last_mon  3last_mon\n",
       "3     4564    4002.0     3458.0     3459.0\n",
       "4     4221    4564.0     4002.0     3458.0\n",
       "5     4529    4221.0     4564.0     4002.0\n",
       "6     4466    4529.0     4221.0     4564.0\n",
       "7     4137    4466.0     4529.0     4221.0\n",
       "..     ...       ...        ...        ...\n",
       "320  12396   14257.0    12640.0    14583.0\n",
       "321  13914   12396.0    14257.0    12640.0\n",
       "322  14174   13914.0    12396.0    14257.0\n",
       "323  15504   14174.0    13914.0    12396.0\n",
       "324  10718   15504.0    14174.0    13914.0\n",
       "\n",
       "[322 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Sales', 'last_mon', '2last_mon', '3last_mon'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1=df.loc[3:200][['last_mon', '2last_mon', '3last_mon']]    #from the dataset we are taking training dataset from index value 3 to 200 .\n",
    "y1=df.loc[3:200]['Sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lm=LinearRegression()                                       # caliing the linear regression class \n",
    "lm.fit(x1,y1)                                               # caliing the fit function and passing the traing dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "440.4902197829897\n",
      "[0.49650255 0.17032679 0.26849197]\n"
     ]
    }
   ],
   "source": [
    "print(lm.intercept_)\n",
    "print(lm.coef_)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we took testing values from the dataframe 'df' from index value 200 to 324"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x5=df.loc[200:400][['last_mon','2last_mon',\t'3last_mon']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>last_mon</th>\n",
       "      <th>2last_mon</th>\n",
       "      <th>3last_mon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>9375.0</td>\n",
       "      <td>9553.0</td>\n",
       "      <td>9977.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>9225.0</td>\n",
       "      <td>9375.0</td>\n",
       "      <td>9553.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>9948.0</td>\n",
       "      <td>9225.0</td>\n",
       "      <td>9375.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>8758.0</td>\n",
       "      <td>9948.0</td>\n",
       "      <td>9225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>10839.0</td>\n",
       "      <td>8758.0</td>\n",
       "      <td>9948.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>14257.0</td>\n",
       "      <td>12640.0</td>\n",
       "      <td>14583.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>12396.0</td>\n",
       "      <td>14257.0</td>\n",
       "      <td>12640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>13914.0</td>\n",
       "      <td>12396.0</td>\n",
       "      <td>14257.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>14174.0</td>\n",
       "      <td>13914.0</td>\n",
       "      <td>12396.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>15504.0</td>\n",
       "      <td>14174.0</td>\n",
       "      <td>13914.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>125 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     last_mon  2last_mon  3last_mon\n",
       "200    9375.0     9553.0     9977.0\n",
       "201    9225.0     9375.0     9553.0\n",
       "202    9948.0     9225.0     9375.0\n",
       "203    8758.0     9948.0     9225.0\n",
       "204   10839.0     8758.0     9948.0\n",
       "..        ...        ...        ...\n",
       "320   14257.0    12640.0    14583.0\n",
       "321   12396.0    14257.0    12640.0\n",
       "322   13914.0    12396.0    14257.0\n",
       "323   14174.0    13914.0    12396.0\n",
       "324   15504.0    14174.0    13914.0\n",
       "\n",
       "[125 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=lm.predict(x5)             # caliing the predict function from LR class and passing the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200     9225\n",
       "201     9948\n",
       "202     8758\n",
       "203    10839\n",
       "204     7266\n",
       "       ...  \n",
       "320    12396\n",
       "321    13914\n",
       "322    14174\n",
       "323    15504\n",
       "324    10718\n",
       "Name: Sales, Length: 125, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df.loc[200:400]['Sales']      # creating a list of 'sales' value from index value 200-400.\n",
    "y"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "earlier we created a  new dataframe df3 for taking the index value from 200 to 324 as wwe want to print our predicted values from 200 to 324.\n",
    "\"df3=df.loc[200:400,]\" but we can also directly pick up the index value from 200_400."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x197ad2671f0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df.loc[200:400].index, pred,color='red',label='predicted')    #'df.loc[200:400].index'= used to access a subset of the DataFrame's index values from 200 to 400 (inclusive). It represents the x-axis values for the plot. it will plot the  predicted value stored in y vs/ index value.\n",
    "\n",
    "plt.plot(y, color = 'green', label = 'actual')                         #it will plot the actuak values from index 200 to 340\n",
    "plt.xlabel('month')\n",
    "plt.ylabel('Sales)')\n",
    "plt.legend()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}