Forecast_Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {
    "_cell_guid": "0b1de6c8-8a08-44a7-a7b0-2db5d592e26e",
    "_uuid": "82c4adf0-5e56-44e0-a0d1-d57f319c93ae",
    "execution": {
     "iopub.execute_input": "2022-08-23T02:24:04.473208Z",
     "iopub.status.busy": "2022-08-23T02:24:04.472903Z",
     "iopub.status.idle": "2022-08-23T02:24:04.486084Z",
     "shell.execute_reply": "2022-08-23T02:24:04.485077Z",
     "shell.execute_reply.started": "2022-08-23T02:24:04.473184Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {
    "_cell_guid": "fd72ad13-2cfb-47da-bfe8-7711408191fc",
    "_uuid": "3eb54478-7ca8-48a1-9996-4fd3e914bb1b",
    "execution": {
     "iopub.execute_input": "2022-08-23T02:24:04.487579Z",
     "iopub.status.busy": "2022-08-23T02:24:04.487294Z",
     "iopub.status.idle": "2022-08-23T02:24:04.494167Z",
     "shell.execute_reply": "2022-08-23T02:24:04.493559Z",
     "shell.execute_reply.started": "2022-08-23T02:24:04.487556Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.svm import LinearSVR, SVR\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from lightgbm import LGBMRegressor\n",
    "from catboost import CatBoostRegressor\n",
    "import seaborn as sns\n",
    "from sklearn. linear_model import Lasso\n",
    "import joblib\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {
    "_cell_guid": "1d50d8ef-cafd-448d-8b6e-0bd09c96081d",
    "_uuid": "dc2ac1c7-b841-41b1-a439-e920aefc28b3",
    "execution": {
     "iopub.execute_input": "2022-08-23T02:24:04.495593Z",
     "iopub.status.busy": "2022-08-23T02:24:04.495150Z",
     "iopub.status.idle": "2022-08-23T02:24:04.519852Z",
     "shell.execute_reply": "2022-08-23T02:24:04.519018Z",
     "shell.execute_reply.started": "2022-08-23T02:24:04.495567Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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>prod_id</th>\n",
       "      <th>avg price</th>\n",
       "      <th>prod_name</th>\n",
       "      <th>order date</th>\n",
       "      <th>pack sold</th>\n",
       "      <th>order</th>\n",
       "      <th>Season</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>996</td>\n",
       "      <td>4.645859</td>\n",
       "      <td>Tomato Desi 500 Gm</td>\n",
       "      <td>1</td>\n",
       "      <td>7687</td>\n",
       "      <td>6472</td>\n",
       "      <td>winter</td>\n",
       "      <td>march</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>996</td>\n",
       "      <td>10.222268</td>\n",
       "      <td>Tomato Desi 500 Gm</td>\n",
       "      <td>2</td>\n",
       "      <td>2974</td>\n",
       "      <td>2434</td>\n",
       "      <td>winter</td>\n",
       "      <td>march</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>996</td>\n",
       "      <td>16.719044</td>\n",
       "      <td>Tomato Desi 500 Gm</td>\n",
       "      <td>3</td>\n",
       "      <td>1568</td>\n",
       "      <td>1171</td>\n",
       "      <td>winter</td>\n",
       "      <td>march</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>996</td>\n",
       "      <td>16.853556</td>\n",
       "      <td>Tomato Desi 500 Gm</td>\n",
       "      <td>4</td>\n",
       "      <td>1700</td>\n",
       "      <td>1195</td>\n",
       "      <td>winter</td>\n",
       "      <td>march</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>996</td>\n",
       "      <td>16.427644</td>\n",
       "      <td>Tomato Desi 500 Gm</td>\n",
       "      <td>5</td>\n",
       "      <td>1549</td>\n",
       "      <td>1078</td>\n",
       "      <td>winter</td>\n",
       "      <td>march</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   prod_id  avg price           prod_name  order date  pack sold  order  \\\n",
       "0      996   4.645859  Tomato Desi 500 Gm           1       7687   6472   \n",
       "1      996  10.222268  Tomato Desi 500 Gm           2       2974   2434   \n",
       "2      996  16.719044  Tomato Desi 500 Gm           3       1568   1171   \n",
       "3      996  16.853556  Tomato Desi 500 Gm           4       1700   1195   \n",
       "4      996  16.427644  Tomato Desi 500 Gm           5       1549   1078   \n",
       "\n",
       "   Season  Month  \n",
       "0  winter  march  \n",
       "1  winter  march  \n",
       "2  winter  march  \n",
       "3  winter  march  \n",
       "4  winter  march  "
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data=pd.read_csv('https://raw.githubusercontent.com/Chitkara-University-Pb/Binary-Brains/main/ModifiedDatasetTomato.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {
    "_cell_guid": "0d7818c2-bc56-475a-aaad-f7fd53e7b699",
    "_uuid": "9798ce2a-ce57-4668-9403-da3b624545ce",
    "execution": {
     "iopub.execute_input": "2022-08-23T02:24:04.522117Z",
     "iopub.status.busy": "2022-08-23T02:24:04.521864Z",
     "iopub.status.idle": "2022-08-23T02:24:04.540565Z",
     "shell.execute_reply": "2022-08-23T02:24:04.539856Z",
     "shell.execute_reply.started": "2022-08-23T02:24:04.522094Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "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>prod_id</th>\n",
       "      <th>avg price</th>\n",
       "      <th>prod_name</th>\n",
       "      <th>order date</th>\n",
       "      <th>pack sold</th>\n",
       "      <th>order</th>\n",
       "      <th>Season</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>996</td>\n",
       "      <td>4.645859</td>\n",
       "      <td>Tomato Desi 500 Gm</td>\n",
       "      <td>1</td>\n",
       "      <td>7687</td>\n",
       "      <td>6472</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>996</td>\n",
       "      <td>10.222268</td>\n",
       "      <td>Tomato Desi 500 Gm</td>\n",
       "      <td>2</td>\n",
       "      <td>2974</td>\n",
       "      <td>2434</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>996</td>\n",
       "      <td>16.719044</td>\n",
       "      <td>Tomato Desi 500 Gm</td>\n",
       "      <td>3</td>\n",
       "      <td>1568</td>\n",
       "      <td>1171</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>996</td>\n",
       "      <td>16.853556</td>\n",
       "      <td>Tomato Desi 500 Gm</td>\n",
       "      <td>4</td>\n",
       "      <td>1700</td>\n",
       "      <td>1195</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>996</td>\n",
       "      <td>16.427644</td>\n",
       "      <td>Tomato Desi 500 Gm</td>\n",
       "      <td>5</td>\n",
       "      <td>1549</td>\n",
       "      <td>1078</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   prod_id  avg price           prod_name  order date  pack sold  order  \\\n",
       "0      996   4.645859  Tomato Desi 500 Gm           1       7687   6472   \n",
       "1      996  10.222268  Tomato Desi 500 Gm           2       2974   2434   \n",
       "2      996  16.719044  Tomato Desi 500 Gm           3       1568   1171   \n",
       "3      996  16.853556  Tomato Desi 500 Gm           4       1700   1195   \n",
       "4      996  16.427644  Tomato Desi 500 Gm           5       1549   1078   \n",
       "\n",
       "   Season  Month  \n",
       "0       1      3  \n",
       "1       1      3  \n",
       "2       1      3  \n",
       "3       1      3  \n",
       "4       1      3  "
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#encoding season column\n",
    "data.replace({'Season':{'winter':1,'summer':2,'monsoon':3}},inplace=True)\n",
    "data.replace({'Month':{ \n",
    "        'march': 3,\n",
    "        'april': 4,\n",
    "        'may': 5,\n",
    "        'june': 6,\n",
    "        'july': 7,\n",
    "        'august': 8}},inplace=True)\n",
    "              \n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {
    "_cell_guid": "c27f218d-6ddb-4d45-86e5-877360b93282",
    "_uuid": "c6e782ea-5118-449a-8e22-ccb25a42a0ea",
    "execution": {
     "iopub.execute_input": "2022-08-23T02:24:04.542063Z",
     "iopub.status.busy": "2022-08-23T02:24:04.541634Z",
     "iopub.status.idle": "2022-08-23T02:24:04.549854Z",
     "shell.execute_reply": "2022-08-23T02:24:04.548986Z",
     "shell.execute_reply.started": "2022-08-23T02:24:04.542038Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "#splitting dataset into X and y\n",
    "y = data['avg price']\n",
    "X = data.drop(['avg price','prod_name', 'order', 'prod_id', 'pack sold'], axis=1)\n",
    "#for demand\n",
    "z = data['order']\n",
    "w = data.drop(['avg price', 'order', 'pack sold' , 'prod_id', 'prod_name'], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {
    "_cell_guid": "ef938877-d7f6-4ff6-964c-25b851ceb9eb",
    "_uuid": "18f3f7f9-3a4f-4344-8514-ec90e1489f0c",
    "execution": {
     "iopub.execute_input": "2022-08-23T02:24:04.551343Z",
     "iopub.status.busy": "2022-08-23T02:24:04.551078Z",
     "iopub.status.idle": "2022-08-23T02:24:04.564152Z",
     "shell.execute_reply": "2022-08-23T02:24:04.563342Z",
     "shell.execute_reply.started": "2022-08-23T02:24:04.551313Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "#splitting into testing and trainning\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,train_size=0.6,shuffle=True,random_state=1)\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,train_size=0.6,shuffle=True,random_state=1)\n",
    "w_train,w_test,z_train,z_test=train_test_split(w,z,train_size=0.6,shuffle=True,random_state=1)\n",
    "w_train,w_test,z_train,z_test=train_test_split(w,z,train_size=0.6,shuffle=True,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {
    "_cell_guid": "41861f30-884a-44b2-a21c-594a61e8c184",
    "_uuid": "e8d826a4-fe8c-4d94-a2b9-7ed5bdf15160",
    "execution": {
     "iopub.execute_input": "2022-08-23T02:24:04.565875Z",
     "iopub.status.busy": "2022-08-23T02:24:04.565325Z",
     "iopub.status.idle": "2022-08-23T02:24:05.226595Z",
     "shell.execute_reply": "2022-08-23T02:24:05.225612Z",
     "shell.execute_reply.started": "2022-08-23T02:24:04.565850Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     Linear Regression trained.\n",
      " Linear Regression (L2 Regularization) trained.\n",
      " Linear Regression (L1 Regularization) trained.\n",
      "                   K-Nearest Neighbors trained.\n",
      "                        Neural Network trained.\n",
      "Support Vector Machine (Linear Kernel) trained.\n",
      "   Support Vector Machine (RBF Kernel) trained.\n",
      "                         Decision Tree trained.\n",
      "                         Random Forest trained.\n",
      "                     Gradient Boosting trained.\n",
      "                               XGBoost trained.\n",
      "                              LightGBM trained.\n",
      "                              CatBoost trained.\n",
      "                     Linear Regression R^2 Score: 0.24871\n",
      " Linear Regression (L2 Regularization) R^2 Score: 0.23281\n",
      " Linear Regression (L1 Regularization) R^2 Score: 0.05223\n",
      "                   K-Nearest Neighbors R^2 Score: 0.39945\n",
      "                        Neural Network R^2 Score: -0.56356\n",
      "Support Vector Machine (Linear Kernel) R^2 Score: -0.39850\n",
      "   Support Vector Machine (RBF Kernel) R^2 Score: -0.04952\n",
      "                         Decision Tree R^2 Score: 0.96899\n",
      "                         Random Forest R^2 Score: 0.94430\n",
      "                     Gradient Boosting R^2 Score: 0.90422\n",
      "                               XGBoost R^2 Score: 0.92448\n",
      "                              LightGBM R^2 Score: 0.78978\n",
      "                              CatBoost R^2 Score: 0.91726\n"
     ]
    }
   ],
   "source": [
    "models = {\n",
    "    \"                     Linear Regression\": LinearRegression(),\n",
    "    \" Linear Regression (L2 Regularization)\": Ridge(),\n",
    "    \" Linear Regression (L1 Regularization)\": Lasso(),\n",
    "    \"                   K-Nearest Neighbors\": KNeighborsRegressor(),\n",
    "    \"                        Neural Network\": MLPRegressor(),\n",
    "    \"Support Vector Machine (Linear Kernel)\": LinearSVR(),\n",
    "    \"   Support Vector Machine (RBF Kernel)\": SVR(),\n",
    "    \"                         Decision Tree\": DecisionTreeRegressor(),\n",
    "    \"                         Random Forest\": RandomForestRegressor(),\n",
    "    \"                     Gradient Boosting\": GradientBoostingRegressor(),\n",
    "    \"                               XGBoost\": XGBRegressor(),\n",
    "    \"                              LightGBM\": LGBMRegressor(),\n",
    "    \"                              CatBoost\": CatBoostRegressor(verbose=0)\n",
    "}\n",
    "\n",
    "for name, model in models.items():\n",
    "    model.fit(X_train, y_train)\n",
    "    print(name + \" trained.\")\n",
    "\n",
    "for name, model in models.items():\n",
    "    print(name + \" R^2 Score: {:.5f}\".format(model.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {
    "_cell_guid": "71f44fbb-8a1f-4f88-9467-ddbc0aa037cc",
    "_uuid": "ff4a8204-e254-4cd1-a92c-57838119a8f8",
    "execution": {
     "iopub.execute_input": "2022-08-23T02:24:05.229696Z",
     "iopub.status.busy": "2022-08-23T02:24:05.229429Z",
     "iopub.status.idle": "2022-08-23T02:24:05.615798Z",
     "shell.execute_reply": "2022-08-23T02:24:05.614889Z",
     "shell.execute_reply.started": "2022-08-23T02:24:05.229671Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostRegressor at 0x1a658e8a500>"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model selection linear regression\n",
    "price_reg_model= CatBoostRegressor(verbose=0)\n",
    "price_reg_model.fit(X_train,y_train)\n",
    "\n",
    "order_reg_model = CatBoostRegressor(verbose=0)\n",
    "order_reg_model.fit(w_train,z_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {
    "_cell_guid": "e391537c-dd5c-4ce2-b68b-fa945e8a62ee",
    "_uuid": "84f14a96-0b06-4cec-b7ea-b24a9bd5b603",
    "execution": {
     "iopub.execute_input": "2022-08-23T02:24:05.619469Z",
     "iopub.status.busy": "2022-08-23T02:24:05.618850Z",
     "iopub.status.idle": "2022-08-23T02:24:05.625375Z",
     "shell.execute_reply": "2022-08-23T02:24:05.624630Z",
     "shell.execute_reply.started": "2022-08-23T02:24:05.619441Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "#Training data prediction\n",
    "price_data_pred= price_reg_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {
    "_cell_guid": "7fb72306-18b9-4ac9-95e9-1584f946b286",
    "_uuid": "3fd219f4-0577-4c81-9489-a2a8dade28a3",
    "execution": {
     "iopub.execute_input": "2022-08-23T02:24:05.627154Z",
     "iopub.status.busy": "2022-08-23T02:24:05.626478Z",
     "iopub.status.idle": "2022-08-23T02:24:05.637803Z",
     "shell.execute_reply": "2022-08-23T02:24:05.636924Z",
     "shell.execute_reply.started": "2022-08-23T02:24:05.627121Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "order_data_pred= order_reg_model.predict(w_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {
    "_cell_guid": "7cf6f7e4-cdfe-46a1-bce1-3073ca7c47be",
    "_uuid": "e36c2d35-fe94-4237-8eef-6e8301169b28",
    "execution": {
     "iopub.execute_input": "2022-08-23T02:24:05.639630Z",
     "iopub.status.busy": "2022-08-23T02:24:05.639291Z",
     "iopub.status.idle": "2022-08-23T02:24:05.644954Z",
     "shell.execute_reply": "2022-08-23T02:24:05.644293Z",
     "shell.execute_reply.started": "2022-08-23T02:24:05.639604Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9919269171238779\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "error_score_price = metrics.r2_score(y_train,price_data_pred )\n",
    "print (error_score_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {
    "_cell_guid": "43569be7-c65b-40e2-a441-d6346900029c",
    "_uuid": "75b75b79-2e1b-4cf4-b2ed-a0e96773635c",
    "execution": {
     "iopub.execute_input": "2022-08-23T02:24:05.646892Z",
     "iopub.status.busy": "2022-08-23T02:24:05.645963Z",
     "iopub.status.idle": "2022-08-23T02:24:05.656122Z",
     "shell.execute_reply": "2022-08-23T02:24:05.655115Z",
     "shell.execute_reply.started": "2022-08-23T02:24:05.646866Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9928570113073083\n"
     ]
    }
   ],
   "source": [
    "error_score_order = metrics.r2_score(z_train,order_data_pred )\n",
    "print (error_score_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {
    "_cell_guid": "25b9860b-49d7-4f15-9b90-bc190378b341",
    "_uuid": "ca8b49f9-80d9-4e47-a259-e046d40df8f5",
    "execution": {
     "iopub.execute_input": "2022-08-23T02:24:05.657673Z",
     "iopub.status.busy": "2022-08-23T02:24:05.657413Z",
     "iopub.status.idle": "2022-08-23T02:24:05.667294Z",
     "shell.execute_reply": "2022-08-23T02:24:05.666398Z",
     "shell.execute_reply.started": "2022-08-23T02:24:05.657651Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.47916183565203874\n",
      "52062.255250154856\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(y_train, price_data_pred))\n",
    "print(mean_squared_error(z_train, order_data_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {
    "_cell_guid": "f383b5da-a741-415e-9a46-d09eb8999c9a",
    "_uuid": "255d8209-e6f8-4358-ba98-70a12682e631",
    "execution": {
     "iopub.execute_input": "2022-08-23T02:24:05.668988Z",
     "iopub.status.busy": "2022-08-23T02:24:05.668732Z",
     "iopub.status.idle": "2022-08-23T02:24:05.825673Z",
     "shell.execute_reply": "2022-08-23T02:24:05.824794Z",
     "shell.execute_reply.started": "2022-08-23T02:24:05.668958Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#visualize the actual prices and Predicted prices\n",
    "from matplotlib import pyplot as plt\n",
    "#plt.scatter(X_train,y_train, color = 'blue', label = 'Actual')\n",
    "plt.scatter(y_train,price_data_pred)\n",
    "plt.xlabel(\"Actual Price\")\n",
    "plt.ylabel(\"Predicted Price\")\n",
    "plt.title(\"Actual Price Vs Predicted Price\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {
    "_cell_guid": "53bc6913-c7e5-4904-a9b5-cd6984ab6b6f",
    "_uuid": "32b0b58e-b2b3-4a6b-83e9-cdc510fbd93f",
    "execution": {
     "iopub.execute_input": "2022-08-23T02:24:05.827234Z",
     "iopub.status.busy": "2022-08-23T02:24:05.826962Z",
     "iopub.status.idle": "2022-08-23T02:24:05.979658Z",
     "shell.execute_reply": "2022-08-23T02:24:05.978797Z",
     "shell.execute_reply.started": "2022-08-23T02:24:05.827208Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(z_train,order_data_pred)\n",
    "plt.xlabel(\"Actual Demand\")\n",
    "plt.ylabel(\"Predicted Demand\")\n",
    "plt.title(\"Actual Demand Vs Predicted Demand\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {
    "_cell_guid": "4ffe7676-da09-44a4-a69d-14c285b95a39",
    "_uuid": "4dc7af48-d970-4dcc-ba6e-52d42d62b464",
    "execution": {
     "iopub.execute_input": "2022-08-23T02:24:05.981164Z",
     "iopub.status.busy": "2022-08-23T02:24:05.980846Z",
     "iopub.status.idle": "2022-08-23T02:24:05.990016Z",
     "shell.execute_reply": "2022-08-23T02:24:05.989074Z",
     "shell.execute_reply.started": "2022-08-23T02:24:05.981131Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "#Training data prediction\n",
    "price_testing_data_pred= price_reg_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {
    "_cell_guid": "864ced2b-642f-4981-91b9-4724caf233de",
    "_uuid": "f9b8ffeb-763d-4f7b-acce-d800cfcd8479",
    "execution": {
     "iopub.execute_input": "2022-08-23T02:24:05.991690Z",
     "iopub.status.busy": "2022-08-23T02:24:05.991158Z",
     "iopub.status.idle": "2022-08-23T02:24:06.002381Z",
     "shell.execute_reply": "2022-08-23T02:24:06.001290Z",
     "shell.execute_reply.started": "2022-08-23T02:24:05.991658Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "order_testing_data_pred= order_reg_model.predict(w_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {
    "_cell_guid": "37c89140-1a73-474f-b9cd-2f9f56111785",
    "_uuid": "d338be0e-74bb-47a4-b71c-06fa401f9714",
    "execution": {
     "iopub.execute_input": "2022-08-23T02:24:06.003961Z",
     "iopub.status.busy": "2022-08-23T02:24:06.003693Z",
     "iopub.status.idle": "2022-08-23T02:24:06.011654Z",
     "shell.execute_reply": "2022-08-23T02:24:06.010724Z",
     "shell.execute_reply.started": "2022-08-23T02:24:06.003938Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9172581168619351\n",
      "0.9396163897090632\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "error_score_price_test = metrics.r2_score(y_test,price_testing_data_pred )\n",
    "print (error_score_price_test)\n",
    "error_score_order_test = metrics.r2_score(z_test,order_testing_data_pred )\n",
    "print (error_score_order_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {
    "_cell_guid": "56e4bf35-77fb-4fa0-a658-685e4acd98d3",
    "_uuid": "1814f140-944f-4c37-a220-f41c15767bea",
    "execution": {
     "iopub.execute_input": "2022-08-23T02:24:06.012981Z",
     "iopub.status.busy": "2022-08-23T02:24:06.012751Z",
     "iopub.status.idle": "2022-08-23T02:24:06.022693Z",
     "shell.execute_reply": "2022-08-23T02:24:06.021822Z",
     "shell.execute_reply.started": "2022-08-23T02:24:06.012960Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.67848039479575\n",
      "356518.48037940694\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "print(mean_squared_error(y_test, price_testing_data_pred))\n",
    "print(mean_squared_error(z_test, order_testing_data_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {
    "_cell_guid": "2051e45b-d2c4-4e18-9beb-977758589713",
    "_uuid": "34b6934e-5ca5-476e-9dde-ea091012163f",
    "execution": {
     "iopub.execute_input": "2022-08-23T02:24:06.025922Z",
     "iopub.status.busy": "2022-08-23T02:24:06.025593Z",
     "iopub.status.idle": "2022-08-23T02:24:06.179756Z",
     "shell.execute_reply": "2022-08-23T02:24:06.178976Z",
     "shell.execute_reply.started": "2022-08-23T02:24:06.025834Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize the actual prices and Predicted prices\n",
    "from matplotlib import pyplot as plt\n",
    "#plt.scatter(X_train,y_train, color = 'blue', label = 'Actual')\n",
    "plt.scatter(y_test,price_testing_data_pred)\n",
    "plt.xlabel(\"Actual Price\")\n",
    "plt.ylabel(\"Predicted Price\")\n",
    "plt.title(\"Actual Price Vs Predicted Price\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {
    "_cell_guid": "b6d0b5b6-90df-4188-ba6d-9d6851d9f23a",
    "_uuid": "4226c4b4-2cfb-4158-862e-e57c88e6b6f5",
    "execution": {
     "iopub.execute_input": "2022-08-23T02:24:06.180821Z",
     "iopub.status.busy": "2022-08-23T02:24:06.180623Z",
     "iopub.status.idle": "2022-08-23T02:24:06.326920Z",
     "shell.execute_reply": "2022-08-23T02:24:06.326144Z",
     "shell.execute_reply.started": "2022-08-23T02:24:06.180799Z"
    },
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(z_test,order_testing_data_pred)\n",
    "plt.xlabel(\"Actual Demand\")\n",
    "plt.ylabel(\"Predicted Demand\")\n",
    "plt.title(\"Actual Demand Vs Predicted Demand\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_data = pd.read_csv('https://raw.githubusercontent.com/Chitkara-University-Pb/Binary-Brains/main/input.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputPred = input_data.drop([], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [],
   "source": [
    "price_predict_input = price_reg_model.predict(inputPred) #date, season , month\n",
    "order_predict_input = order_reg_model.predict(inputPred) #Avg Price, date, season , month\n",
    "price_predict_input = pd.DataFrame(price_predict_input, columns=['predicted avg price']).to_csv('priceForecast.csv')\n",
    "order_predict_input = pd.DataFrame(order_predict_input, columns=['predicted demand']).to_csv('orderForecast.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PriceOrderForecast.pkl']"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Saving Machine Learning Model\n",
    "import joblib\n",
    "cb = CatBoostRegressor(verbose=0)\n",
    "joblib.dump(cb,\"PriceOrderForecast.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = joblib.load(\"PriceOrderForecast.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "vscode": {
   "interpreter": {
    "hash": "b9e6cb15b86ac2757c8003d41fac53f163cb90e88454a41bdcc40095a21b5065"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}