playground-series.ipynb

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     "text": [
      "/kaggle/input/tabular-playground-series-jan-2022/sample_submission.csv\n",
      "/kaggle/input/tabular-playground-series-jan-2022/train.csv\n",
      "/kaggle/input/tabular-playground-series-jan-2022/test.csv\n"
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   "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"
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   "source": [
    "df = pd.read_csv(\"../input/tabular-playground-series-jan-2022/train.csv\")\n",
    "df1 = pd.read_csv(\"../input/tabular-playground-series-jan-2022/test.csv\")\n"
   ]
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       "   row_id        date  country       store         product  num_sold\n",
       "0       0  2015-01-01  Finland  KaggleMart      Kaggle Mug       329\n",
       "1       1  2015-01-01  Finland  KaggleMart      Kaggle Hat       520\n",
       "2       2  2015-01-01  Finland  KaggleMart  Kaggle Sticker       146\n",
       "3       3  2015-01-01  Finland  KaggleRama      Kaggle Mug       572\n",
       "4       4  2015-01-01  Finland  KaggleRama      Kaggle Hat       911"
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 26298 entries, 0 to 26297\n",
      "Data columns (total 6 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   row_id    26298 non-null  int64 \n",
      " 1   date      26298 non-null  object\n",
      " 2   country   26298 non-null  object\n",
      " 3   store     26298 non-null  object\n",
      " 4   product   26298 non-null  object\n",
      " 5   num_sold  26298 non-null  int64 \n",
      "dtypes: int64(2), object(4)\n",
      "memory usage: 1.2+ MB\n"
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       "             row_id      num_sold\n",
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       "75%    19722.750000    510.000000\n",
       "max    26297.000000   2884.000000"
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       "         date  country       store         product  num_sold\n",
       "0  2015-01-01  Finland  KaggleMart      Kaggle Mug       329\n",
       "1  2015-01-01  Finland  KaggleMart      Kaggle Hat       520\n",
       "2  2015-01-01  Finland  KaggleMart  Kaggle Sticker       146\n",
       "3  2015-01-01  Finland  KaggleRama      Kaggle Mug       572\n",
       "4  2015-01-01  Finland  KaggleRama      Kaggle Hat       911"
      ]
     },
     "execution_count": 6,
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   "source": [
    "df = df.iloc[:, 1:]\n",
    "df.head()"
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       "           num_sold\n",
       "count  26298.000000\n",
       "mean     387.533577\n",
       "std      266.076193\n",
       "min       70.000000\n",
       "25%      190.000000\n",
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    {
     "data": {
      "text/plain": [
       "Finland    8766\n",
       "Norway     8766\n",
       "Sweden     8766\n",
       "Name: country, dtype: int64"
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   "id": "82245f4f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T08:53:46.610933Z",
     "iopub.status.busy": "2022-03-21T08:53:46.610088Z",
     "iopub.status.idle": "2022-03-21T08:53:47.149620Z",
     "shell.execute_reply": "2022-03-21T08:53:47.150083Z",
     "shell.execute_reply.started": "2022-03-21T08:44:42.072530Z"
    },
    "papermill": {
     "duration": 0.596058,
     "end_time": "2022-03-21T08:53:47.150259",
     "exception": false,
     "start_time": "2022-03-21T08:53:46.554201",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df.hist(bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1dfe7591",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T08:53:47.210076Z",
     "iopub.status.busy": "2022-03-21T08:53:47.194301Z",
     "iopub.status.idle": "2022-03-21T08:53:47.381370Z",
     "shell.execute_reply": "2022-03-21T08:53:47.381745Z",
     "shell.execute_reply.started": "2022-03-21T08:44:42.387016Z"
    },
    "papermill": {
     "duration": 0.213229,
     "end_time": "2022-03-21T08:53:47.381871",
     "exception": false,
     "start_time": "2022-03-21T08:53:47.168642",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4fed72e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T08:53:47.428818Z",
     "iopub.status.busy": "2022-03-21T08:53:47.425969Z",
     "iopub.status.idle": "2022-03-21T08:53:47.470927Z",
     "shell.execute_reply": "2022-03-21T08:53:47.470505Z",
     "shell.execute_reply.started": "2022-03-21T08:44:42.603776Z"
    },
    "papermill": {
     "duration": 0.070444,
     "end_time": "2022-03-21T08:53:47.471054",
     "exception": false,
     "start_time": "2022-03-21T08:53:47.400610",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['country'].replace(['Finland', 'Norway', 'Sweden'], [1, 2, 3], inplace=True)\n",
    "df['store'].replace(['KaggleMart', 'KaggleRama'], [1, 2], inplace=True)\n",
    "df['product'].replace(['Kaggle Mug', 'Kaggle Hat', 'Kaggle Sticker'], [1, 2, 3], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "37f1832f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T08:53:47.513516Z",
     "iopub.status.busy": "2022-03-21T08:53:47.512973Z",
     "iopub.status.idle": "2022-03-21T08:53:48.320470Z",
     "shell.execute_reply": "2022-03-21T08:53:48.321169Z",
     "shell.execute_reply.started": "2022-03-21T08:44:42.660102Z"
    },
    "papermill": {
     "duration": 0.830937,
     "end_time": "2022-03-21T08:53:48.321400",
     "exception": false,
     "start_time": "2022-03-21T08:53:47.490463",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = df.drop(['date', 'num_sold'], axis=1)  \n",
    "y = df['num_sold']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "24c5d9a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T08:53:48.398681Z",
     "iopub.status.busy": "2022-03-21T08:53:48.397919Z",
     "iopub.status.idle": "2022-03-21T08:53:48.403713Z",
     "shell.execute_reply": "2022-03-21T08:53:48.404314Z",
     "shell.execute_reply.started": "2022-03-21T08:44:42.673540Z"
    },
    "papermill": {
     "duration": 0.048888,
     "end_time": "2022-03-21T08:53:48.404606",
     "exception": false,
     "start_time": "2022-03-21T08:53:48.355718",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>store</th>\n",
       "      <th>product</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  store  product\n",
       "0        1      1        1\n",
       "1        1      1        2\n",
       "2        1      1        3\n",
       "3        1      2        1\n",
       "4        1      2        2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "922634c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T08:53:48.476731Z",
     "iopub.status.busy": "2022-03-21T08:53:48.475860Z",
     "iopub.status.idle": "2022-03-21T08:53:48.547652Z",
     "shell.execute_reply": "2022-03-21T08:53:48.547175Z",
     "shell.execute_reply.started": "2022-03-21T08:44:42.688022Z"
    },
    "papermill": {
     "duration": 0.109751,
     "end_time": "2022-03-21T08:53:48.547774",
     "exception": false,
     "start_time": "2022-03-21T08:53:48.438023",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "LR = LogisticRegression(solver='liblinear',max_iter=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "395ea828",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T08:53:48.591787Z",
     "iopub.status.busy": "2022-03-21T08:53:48.591045Z",
     "iopub.status.idle": "2022-03-21T08:54:19.833169Z",
     "shell.execute_reply": "2022-03-21T08:54:19.833807Z",
     "shell.execute_reply.started": "2022-03-21T08:44:42.696755Z"
    },
    "papermill": {
     "duration": 31.266414,
     "end_time": "2022-03-21T08:54:19.833997",
     "exception": false,
     "start_time": "2022-03-21T08:53:48.567583",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(solver='liblinear')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Training\n",
    "LR.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6e7c1200",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T08:54:19.920851Z",
     "iopub.status.busy": "2022-03-21T08:54:19.904723Z",
     "iopub.status.idle": "2022-03-21T08:54:19.997144Z",
     "shell.execute_reply": "2022-03-21T08:54:19.998567Z",
     "shell.execute_reply.started": "2022-03-21T08:45:15.909786Z"
    },
    "papermill": {
     "duration": 0.132253,
     "end_time": "2022-03-21T08:54:19.998777",
     "exception": false,
     "start_time": "2022-03-21T08:54:19.866524",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "pred=LR.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c7dcaa12",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T08:54:20.079893Z",
     "iopub.status.busy": "2022-03-21T08:54:20.079069Z",
     "iopub.status.idle": "2022-03-21T08:54:20.083797Z",
     "shell.execute_reply": "2022-03-21T08:54:20.084225Z",
     "shell.execute_reply.started": "2022-03-21T08:49:00.491031Z"
    },
    "papermill": {
     "duration": 0.034859,
     "end_time": "2022-03-21T08:54:20.084381",
     "exception": false,
     "start_time": "2022-03-21T08:54:20.049522",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.004372623574144487"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, pred)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
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