intern_bio-vision.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Series Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intialization and Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.api import ExponentialSmoothing\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from warnings import filterwarnings\n",
    "filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('intern_project.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Given extracted CSV File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>ELCOT PO DATE</th>\n",
       "      <th>Total Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13/3/2017</td>\n",
       "      <td>505,751.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11/5/2017</td>\n",
       "      <td>125,322.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19/5/2017</td>\n",
       "      <td>1,163,392.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30/6/2017</td>\n",
       "      <td>148,486.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8/2/2018</td>\n",
       "      <td>2,586,544.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>16/5/2023</td>\n",
       "      <td>69,054.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>16/5/2023</td>\n",
       "      <td>196,258.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>16/5/2023</td>\n",
       "      <td>105,398.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>16/5/2023</td>\n",
       "      <td>148,876.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>16/6/2023</td>\n",
       "      <td>617,810.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    ELCOT PO DATE  Total Value \n",
       "0       13/3/2017    505,751.00\n",
       "1       11/5/2017    125,322.00\n",
       "2       19/5/2017  1,163,392.00\n",
       "3       30/6/2017    148,486.00\n",
       "4        8/2/2018  2,586,544.00\n",
       "..            ...           ...\n",
       "176     16/5/2023     69,054.00\n",
       "177     16/5/2023    196,258.00\n",
       "178     16/5/2023    105,398.00\n",
       "179     16/5/2023    148,876.00\n",
       "180     16/6/2023    617,810.00\n",
       "\n",
       "[181 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 181 entries, 0 to 180\n",
      "Data columns (total 2 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   ELCOT PO DATE  181 non-null    object\n",
      " 1    Total Value   181 non-null    object\n",
      "dtypes: object(2)\n",
      "memory usage: 3.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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",
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       "    }\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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ELCOT PO DATE</th>\n",
       "      <td>181</td>\n",
       "      <td>88</td>\n",
       "      <td>16/5/2023</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Value</th>\n",
       "      <td>181</td>\n",
       "      <td>172</td>\n",
       "      <td>387,552.00</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count unique         top freq\n",
       "ELCOT PO DATE   181     88   16/5/2023   13\n",
       " Total Value    181    172  387,552.00    4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(181, 2)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ELCOT PO DATE', ' Total Value '], dtype='object')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### converting the string into integer in the \\'Total Value\\'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting the string into integer in the \\'Total Value\\' \n",
    "\n",
    "total_value = np.array(data[' Total Value '])\n",
    "l = []\n",
    "for i in total_value:\n",
    "    \n",
    "    def number(i)->int:\n",
    "        c = 0\n",
    "        for k in range(len(i)):\n",
    "            if i[k] == '.':\n",
    "                break\n",
    "            if i[k] == ' ':\n",
    "                continue\n",
    "            if i[k] >= '0' and i[k] <= '9':\n",
    "                c = c * 10 + (ord(i[k]) - 48)\n",
    "        return c\n",
    "    \n",
    "    check = number(i)\n",
    "    l.append(check)\n",
    "    \n",
    "#print(*l,sep='\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total Value List and Date inserted into Data Frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#print(type(A))\n",
    "D = pd.DataFrame({'Date':data['ELCOT PO DATE'], 'Value':l})\n",
    "type(D)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating New CSV File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "D.to_csv('mlcreate_csv.csv',index=False)\n",
    "\n",
    "df = pd.read_csv('mlcreate_csv.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Created New CSV File use for Time Series Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Date    Value\n",
      "0  13/3/2017   505751\n",
      "1  11/5/2017   125322\n",
      "2  19/5/2017  1163392\n",
      "3  30/6/2017   148486\n",
      "4   8/2/2018  2586544\n",
      "\n",
      "          Date   Value\n",
      "176  16/5/2023   69054\n",
      "177  16/5/2023  196258\n",
      "178  16/5/2023  105398\n",
      "179  16/5/2023  148876\n",
      "180  16/6/2023  617810\n"
     ]
    }
   ],
   "source": [
    "#file name : csv_df\n",
    "print(df.head())\n",
    "print()\n",
    "print(df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 181 entries, 0 to 180\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   Date    181 non-null    object\n",
      " 1   Value   181 non-null    int64 \n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 3.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Value</th>\n",
       "      <td>181.0</td>\n",
       "      <td>1.055930e+06</td>\n",
       "      <td>1.253377e+06</td>\n",
       "      <td>566.0</td>\n",
       "      <td>240271.0</td>\n",
       "      <td>669749.0</td>\n",
       "      <td>1196827.0</td>\n",
       "      <td>6933268.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count          mean           std    min       25%       50%  \\\n",
       "Value  181.0  1.055930e+06  1.253377e+06  566.0  240271.0  669749.0   \n",
       "\n",
       "             75%        max  \n",
       "Value  1196827.0  6933268.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             Value\n",
      "Date              \n",
      "13/3/2017   505751\n",
      "11/5/2017   125322\n",
      "19/5/2017  1163392\n",
      "30/6/2017   148486\n",
      "8/2/2018   2586544\n"
     ]
    }
   ],
   "source": [
    "df.index = df['Date']\n",
    "del df['Date']\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 181 entries, 13/3/2017 to 16/6/2023\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype\n",
      "---  ------  --------------  -----\n",
      " 0   Value   181 non-null    int64\n",
      "dtypes: int64(1)\n",
      "memory usage: 2.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_size = int(len(df) * 0.8)\n",
    "train_data = df.iloc[:train_size]\n",
    "test_data = df.iloc[train_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ExponentialSmoothing(train_data['Value'], seasonal='additive', seasonal_periods=12)\n",
    "fitted_model = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast_periods = len(test_data)\n",
    "forecast_values = fitted_model.forecast(forecast_periods)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(train_data.index, train_data['Value'], label='Train data')\n",
    "plt.plot(test_data.index, test_data['Value'], label='Test data')\n",
    "plt.plot(test_data.index, forecast_values, label='Forecast')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Value')\n",
    "plt.title('Time Series Forecasting')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Data_Science",
   "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": "3.11.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}